Distributionally Robust Hydrogen Optimization With Ensured Security and Multi-Energy Couplings

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

Chapter 4 - Data-driven robust stochastic optimization for power systems operations

Guest Editorial: Situational awareness of integrated energy systems

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This article proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modeled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the two-stage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This article can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system.

The joint operation of natural gas system and power system is an important direction of energy system integration. In this paper, the unit combination modeling method of integrated gas-electric energy system is studied, and the operation constraints such as output, climbing and duration considering unit startup and shutdown state are established. The matrix balance model of power system and natural gas system is established. Based on the generation transfer distribution factor, the tie line security constraints are proposed, and the simplified method of gas electric coupling model of gas turbine based on the piecewise linearization method is proposed. Finally, 6-node power system and 6-node natural gas system are used to build an integrated gas electric energy system. Taking the economic cost optimization as the optimization goal, it is verified that the unit combination constraint in this paper can effectively optimize the unit operation dispatching in the combined system.

Corrigendum: Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs with P2G Storage and Wind PowerName of all authors as they appear in the published original article Affiliations of all authors as they appear in the published original version of the article * Correspondence: Tao Jiang, tjiang@neepu.edu.cnKeywords: Power-to-gas, ramping cost, wind power accommodation, stochastic programming, natural gas systemCorrigendum on: Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs With Power-to-Gas Storage and Wind Power. Front. Energy Res. 8:596774. doi: 10.3389/fenrg.2020.596774. Incorrect Funder NameThere is an error in the Funding statement. The correct Name for the Funder is ** science and technology project of State Grid Jilin Corporation (SGJL0000DKJS2000287)**. This is the only funder. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

To use more green electricity, more renewable energy sources are employed in the distribution power systems. The combination of wind power with the hydrogen electrolyzer unit is one of effective ways to utilize renewable energy by converting electricity to hydrogen. In addition, more natural gas-fired units increase the couplings between the power systems and the natural gas systems. Therefore, it is necessary to conduct coordinated scheduling for integrated power and natural gas systems in consideration of hydrogen electrolyzer units that are used to produce hydrogen from wind power. The revenue from hydrogen production is established as a function of a part of wind power. To consider the distribution topology, the spanning tree constraints are used to ensure the radiality of the distribution system. The DistFlow model and the piecewise linear method are used to deal with the nonlinear models of the distribution system and the natural gas system. Furthermore, the carbon emission is included in the optimization objective to guarantee an optimal carbon emission with an optimal operational point. The entire problem is established as mixed integer linear programming model. Tests on a revised IEEE 33-bus system associated with a 20-node natural gas system show that the coordinated scheduling on integrated power and natural gas systems with hydrogen production from wind power can achieve an optimal operational cost.

The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics. This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub, with collocated gas-fired units, power-to-gas (PtG) facilities, and natural gas storages, is considered to store or convert one type of energy (i.e., electricity or natural gas) into the other form, which could analogously function as large-scale electrical energy storages. The column-and-constraint generation (C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.

This paper proposes a novel optimal planning model for combined heat and power (CHP) in multiple energy systems of natural gas and electricity to benefit both networks by deferring investment for network owners and reducing use-of-system (UoS) charge for network users. The new planning model considers the technical constraints of both electricity and natural gas systems. A two-stage planning approach is proposed to determine the optimal site and size of CHPs. In the first stage, a long-run incremental cost matrix is designed to reflect CHP locational impact on both natural gas and electricity network investment, used as a criterion to choose the optimal location. In the second stage, CHP size is determined by solving an integrated optimal model with the objective to minimize total incremental network investment costs. The proposed method is resolved by the interior-point method and implemented on a practically integrated electricity and natural gas systems. Two case studies are conducted to test the performance for single and multiple CHPs cases. This paper enables cost-efficient CHP planning to benefit integrated natural gas and electricity networks and network users in terms of reduced network investment cost and consequently reduced UoS charges.

Integrated electricity and natural gas system (IEGS) plays an important role in increasing efficiency of energy usage. Uncertain power flow calculation is helpful to system optimization and scheduling with increasingly prominent uncertainty. In this paper, an affine energy flow algorithm for integrated electricity and natural gas system is proposed to analyze the system uncertainty. First, the affine models and energy flow equations of the system are established. Secondly, the prediction-correction affine energy flow algorithm is proposed. The algorithm predicts the state variable according to the sensitivity analysis, and introduces correction coefficients to compress the predicted state variable. At the same time, an improved decomposition method of multi energy flow is adopted, without considering the calculation order of multi energy flow in the complex coupling network. Finally, the case simulation verifies the advantages of the affine power flow method in conservatism, computational efficiency and tracking the source of uncertainty.

Quay cranes are among the most important resources in port terminals, and their efficient use is crucial for port terminals to remain competitive in the market. The problem of determining the sequence of tasks performed by the quay cranes that minimizes the turnaround time of a vessel is an NP‐hard problem known as the quay crane scheduling problem (QCSP).In this paper, we consider the unidirectional QCSP under uncertain processing times, where the cranes are only allowed to move in a specific direction. We start by presenting the first distributionally robust optimization (DRO) model for this problem and an exact decomposition algorithm to solve it. The proposed DRO model makes it possible to derive a stochastic programming model and a robust optimization model for the unidirectional QCSP by an appropriate choice of the risk‐averse parameter of the model. Through extensive numerical results, we compare these three approaches—stochastic programming, robust optimization, and DRO—to investigate whether significant differences among the solutions obtained exist. Finally, we propose a new method for helping practitioners to determine a representative set of different DRO solutions.

Co-operation of electricity and natural gas systems including electric vehicles and variable renewable energy sources based on a continuous-time model approach

Identifying the must‐run capacity (MRC) could help to accelerate the unit commitment and assess market power, thus critical for achieving an efficient power system dispatch. However, the existing approaches identify the MRC without considering the ever‐strengthening interdependence between power system and natural gas (NG) system, as well as neglecting either the uncertainties of renewables or the constraints on start‐up/shut‐down states for thermal units. To fill the gaps, this article proposes a novel model and methodology for identifying the MRC in power system considering the interdependence with NG system, with adopting a distributionally robust (DR) approach to consider the uncertainties of renewables. A constraint‐generation based algorithm is devised to solve the model, with a relax‐round‐polish based Alternating Direction Method of Multipliers (ADMM) algorithm to address the interaction between the two energy systems. Comparative studies show the effectiveness in MRC assessment over the existing approaches.

The increasing penetration of natural gas power generation and developing power-to-gas (P2G) technique in recent years have promoted the coupling between the power supply system and the natural gas system than ever before. Given the background, this paper studies the collaborative planning of integrated energy system with gas turbines and P2G stations. First, the energy hub concept is introduced and modeled. A model for an integrated energy system with multiple interconnected energy hubs is next presented. Then, this paper takes the minimum overall cost as planning objectives including investment cost, operation cost and energy shortage cost characterizing the reliability of the integrated energy system, optimizes the capacities of wind generation units, traditional generation units, gas turbines, P2G stations with particle swarm optimization. Case study verified the effectiveness of collaborative planning in economy and reliability of integrated systems.

The increasing penetration of inverter-based renewable energy sources has fundamentally altered the dynamic behavior of modern power systems by displacing conventional synchronous generation. Reduced system inertia, higher rates of change of frequency, modified fault responses, and complex voltage regulation have emerged as critical stability challenges in low-inertia grids. This paper presents a comprehensive study of power system stability under high renewable energy penetration through a structured literature review, a unified methodological framework, and a realistic transmission-level case study. The proposed framework integrates grid characterization, component and control modeling, multi-domain stability assessment, contingency analysis, and coordinated mitigation strategies using both phasor-domain and electromagnetic transient simulations. A three-zone network with varying levels of renewable integration is analyzed to quantify impacts on frequency, voltage, and small-signal stability following severe contingencies. Results demonstrate that stability degradation increases nonlinearly with renewable share in baseline grid-following configurations. The deployment of grid-forming inverter controls, synthetic inertia, and fast frequency response from battery energy storage significantly improves system performance by reducing RoCoF, restoring frequency nadir, and enhancing voltage recovery. Eigenvalue analysis further confirms improved damping of critical interaction modes. The findings highlight the necessity of hybrid control architectures and coordinated mitigation strategies to ensure reliable operation of future power systems with high renewable penetration.