arXiv subjects

Shahab Dehghan

Publications and source records attributed to Shahab Dehghan.

5 recordsLinked to original sources

arXiv2026

Frequency Security-Aware Production Scheduling of Utility-Scale Off-Grid Renewable P2H Systems Coordinating Heterogeneous Electrolyzers

Renewable power-to-hydrogen (ReP2H) enables large-scale renewable energy utilization and supports the decarbonization of hard-to-abate sectors, such as chemicals and maritime transport, via hydrogen-based renewable ammonia and methanol fuels. As a result, utility-scale ReP2H projects are expanding worldwide. However, off-grid ReP2H systems exhibit low inertia due to their converter-dominated nature, making frequency security a critical concern. Although recent studies show that electrolyzers can contribute to frequency regulation (FR), their support capability depends on operating states and loading levels, creating a trade-off between hydrogen output and frequency security. To address this challenge, this work develops a unified co-optimization framework for frequency security-aware production scheduling of utility-scale off-grid ReP2H systems coordinating heterogeneous electrolyzers. A system-level frequency response model is established to capture multi-stage FR from alkaline water electrolyzers (AWEs), proton exchange membrane electrolyzers (PEMELs), and other resources, including ammonia-fueled generators retrofitted in co-located chemical plants, battery energy storage, and wind turbines (WTs). Stage-wise transient frequency security constraints are derived, reformulated into tractable forms, and embedded into production scheduling, enabling coordinated on/off switching and load allocation across electrolyzers to maximize hydrogen output under uncertain renewable power input while enforcing frequency security constraints. Case studies based on real-world systems demonstrate that the proposed approach allows HPs to replace 55.52% and 96.85% of FR reserves from WTs and AFGs, respectively, while maintaining comparable hydrogen output. Year-long simulations show an average 28.96% increase in annual net profit resulting from reduced reliance on conventional reserves.

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arXiv2023

Inertia-Aware Microgrid Investment Planning Using Tractable Decomposition Algorithms

The integration of the frequency dynamics into Micro-Grid (MG) investment and operational planning problems is vital in ensuring the security of the system in the post-contingency states. However, the task of including transient security constraints in MG planning problems is non-trivial. This is due to the highly non-linear and non-convex nature of the analytical closed form of the frequency metrics (e.g., frequency nadir) and power flow constraints. To handle this issue, this paper presents two algorithms for decomposing the MG investment planning problem into multiple levels to enhance computational tractability and optimality. Furthermore, the sensitivity of the decisions made at each level is captured by corresponding dual cutting planes to model feasible secure regions. This, in turn, ensures both the optimal determination and placement of inertia services and accelerates the convergence of the proposed decomposition algorithms. The efficient and effective performance of the proposed algorithms is tested and verified on an 18-bus Low Voltage (LV) network and a 30-bus Medium Voltage (MV) network under various operating scenarios.

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arXiv2022

Multi-model assessment of heat decarbonisation options in the UK using electricity and hydrogen

Delivering low-carbon heat will require the substitution of natural gas with low-carbon alternatives such as electricity and hydrogen. The objective of this paper is to develop a method to soft-link two advanced, investment-optimising energy system models, RTN (Resource-Technology Network) and WeSIM (Whole-electricity System Investment Model), in order to assess cost-efficient heat decarbonisation pathways for the UK while utilising the respective strengths of the two models. The linking procedure included passing on hourly electricity prices from WeSIM as input to RTN, and returning capacities and locations of hydrogen generation and shares of electricity and hydrogen in heat supply from RTN to WeSIM. The outputs demonstrate that soft-linking can improve the quality of the solution, while providing useful insights into the cost-efficient pathways for zero-carbon heating. Quantitative results point to the cost-effectiveness of using a mix of electricity and hydrogen technologies for delivering zero-carbon heat, also demonstrating a high level of interaction between electricity and hydrogen infrastructure in a zero-carbon system. Hydrogen from gas reforming with carbon capture and storage can play a significant role in the medium term, while remaining a cost-efficient option for supplying peak heat demand in the longer term, with the bulk of heat demand being supplied by electric heat pumps.

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arXiv2020

Towards a Sustainable Microgrid on Alderney Island Using a Python-based Energy Planning Tool

In remote or islanded communities, the use of microgrids (MGs) is necessary to ensure electrification and resilience of supply. However, even in small-scale systems, it is computationally and mathematically challenging to design low-cost, optimal, sustainable solutions taking into consideration all the uncertainties of load demands and power generations from renewable energy sources (RESs). This paper uses the open-source Python-based Energy Planning (PyEPLAN) tool, developed for the design of sustainable MGs in remote areas, on the Alderney island, the 3$^{rd}$ largest of the Channel Islands with a population of about 2000 people. A two-stage stochastic model is used to optimally invest in battery storage, solar power, and wind power units. Moreover, the AC power flow equations are modelled by a linearised version of the DistFlow model in PyEPLAN, where the investment variables are here-and-now decisions and not a function of uncertain parameters while the operation variables are wait-and-see decisions and a function of uncertain parameters. The $k$-means clustering technique is used to generate a set of best (risk-seeker), nominal (risk-neutral), and worst (risk-averse) scenarios capturing the uncertainty spectrum using the yearly historical patterns of load demands and solar/wind power generations. The proposed investment planning tool is a mixed-integer linear programming (MILP) model and is coded with Pyomo in PyEPLAN.

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arXiv2020

A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints

When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-High-Frequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-max-min problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network.

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