Gas fired power plants can provide flexible generation that can help to balance intermittent renewable energy sources as the world moves towards using a more sustainable energy infrastructure. The increasing use of gas-fired power plants for balancing renewable generation creates an interdependence between the electricity and gas networks, where uncertainty can propagate between the two systems, causing insecure operating conditions and disruptions in both gas and electricity supply. Unfortunately, these interactions are computationally challenging to analyze due to the relatively longer transient dynamics of the gas network models. Recent work have provided efficient methods for solving problem as a two stage stochastic optimization problem. In this paper we investigate the importance of using a dynamic gas network model, as compared to a simplified steady-state network model which ignores gas system transients. We find that using the dynamic model allows the networks to better manage the uncertainty of the renewable energy sources and results in lower costs and reduced load shedding.

Gas-fired generators, with their ability to quickly ramp up and down their electricity production, play an important role in managing renewable energy variability. However, these changes in electricity production translate into variability in the consumption of natural gas, and propagation of uncertainty from the electric grid to the natural gas system. To ensure that both systems are operating safely, there is an increasing need for coordination and uncertainty management among the electricity and gas networks. A challenging aspect of this coordination is the consideration of natural gas dynamics, which play an important role in intra-day operation, but give rise to a set of non-linear and non-convex equations that are hard to optimize even in the deterministic case. Ideally, the problem is formulated as a stochastic problem but many conventional methods for stochastic optimization are numerically intractable because they either incorporate a large number of scenarios directly or require the underlying problem to be convex. To address these challenges, we propose using a Stochastic Hybrid Approximation algorithm to more efficiently solve these problems and investigate the efficacy of several different variants of this algorithm. Our case study demonstrates that the proposed technique is able to quickly obtain high quality solutions and outperforms existing benchmarks such as Generalized Benders Decomposition. We demonstrate that coordinated uncertainty management that accounts for the gas system can significantly reduce both electric and gas system load shed in stressed conditions.

The increasing use of gas-fired power plants requires a more thorough consideration of the interdependencies between power and gas systems. Ideally, these systems should be dispatched in a fully coordinated manner. However, they are generally operated as separate entities and it may not be possible to share complete information regarding the internal variables and constraints of each network. To overcome this limitation, we propose an inter-system flexibility set as a proxy to provide information about the power system to the gas network, so that actions taken in the gas network, such as load shedding, retain feasibility in the power system.

The trading of electricity and natural gas, according to the current organizational framework, takes place in independent and sequentially cleared trading floors. This setup leads to imperfect coordination between the two energy systems and it also separates the day-ahead scheduling from the real-time balancing decisions. Acknowledging these market inefficiencies, we propose defining standardized contracts in the form of swing options that allow for a flexible demand pattern according to a predetermined price. In this paper, swing option contracts are parameterized and priced on the gas system side and can be concluded by the power generators to hedge against gas price uncertainty. We perform the contract pricing using a stochastic bilevel optimization model that anticipates the reaction of both systems with respect to contract definition. Our analysis shows that if these contracts are properly priced and valuated by the gas and electricity system, respectively, they can improve intersystem, as well as inter-temporal, coordination and thus reduce the expected system cost.

An increasing amount of gas-fired power plants are currently being installed in modern power grids worldwide. This is due to their low cost and the inherent flexibility offered to the electrical network, particularly in the face of increasing renewable generation. However, the integration and operation of gas generators poses additional challenges to gas network operators, mainly because they can induce rapid changes in the demand. This paper presents an efficient minimization scheme of gas compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flows. The optimization scheme is comprised of a set of transient nonlinear partial differential equations that model the isothermal gas flow in pipes, an adjoint problem for efficient calculation of the objective gradients and constraint Jacobians, and state-of-the-art optimal control methods for solving nonlinear programs. As the evaluation of constraint Jacobians can become computationally costly as the number of constraints increases, efficient constraint lumping schemes are proposed and investigated with respect to accuracy and performance. The resulting optimal control problems are solved using both interior-point and sequential quadratic programming methods. The proposed optimization framework is validated through several benchmark cases of increasing complexity.

Given the increasing importance of multi-energy carrier system modeling, this paper focuses on modeling the dynamics that occur in natural gas transmission lines. In high-pressure pipelines, these dynamics are governed by a set of hyperbolic partial differential equations. Dissipative finite volume discretization schemes are proposed that honor the discrete maximum principle. The convergence of the proposed discretization schemes is investigated and compared with existing methods suggested in the literature for natural gas transmission such as the implicit cell-centered method. High order time discretization methods are also tested and their suitability with respect to the maximum principle is discussed.

In many parts of the world, electricity systems are experiencing an increasing amount of gas-fired generation being installed due to its cheap fuel. The flexibility offered by gas generators is considered to be beneficial for electric grids, particularly in the face of increasing renewable generation. However, gas generators are often problematic for the gas network, as they induce large, rapid changes in the demand. The objective of this paper is to investigate the interaction between electrical and gas networks, and how security criteria for gas system operations implicitly impact the operation of the electric grid. Special attention is paid to the incorporation of the gas system dynamics, which have profound impact on the security assessment. To facilitate the assessment, the paper proposes an analysis framework which considers i) preventive security assessment for the gas system, ii) determination of remedial actions and gas load shedding after a contingency occurs, and iii) the impact of the load shedding on the electric grid, given different operating rules for the gas system. A case study shows that the preventive security assessment is beneficial both for the gas and electric grid, and that the security criterion used to determine the required gas system remedial actions impacts the cost of the electric grid.

This paper summarises the modelling results obtained with a sub-wavelength optical packet switching technology, called OPST (Optical Packet Switch and Transport), when compared against an IPoDWDM solution. In particular, the impact of data centre location in the network on the network cost is studied for two architectures based on different technologies. Data centre location, routing strategies, service mix, traffic growth and subscriber service take-up is modelled to obtain a broad view about the cost sensitivities in these networks. The main contribution of the paper is to demonstrate that resilience to data centre location and changing traffic patterns enable the sub-wavelength packet optical solution to achieve cost savings of 150% when compared to the IPoDWDM approach. Such flexibility of the packet optical solution also enables 100-300% of power consumption cost reduction and an average 150% savings on rack cabinets for typical configurations.

In this paper the uncertainties and the implied level of risk associated with next-generation network architectures is modelled using Monte Carlo simulation, aimed at understanding network economics evolution. A high number of network parameters - like incremental network deployment, data centre location, network architecture, service mix, traffic growth and subscriber take-up - are modelled. A wide range of values is used for these parameters to gain understanding of their impact on network cost. Such an approach provides insight into the risk level undertaken by operators when building their network infrastructure based on a specific forecast. Thus, the core result of this analysis is that a sub-wavelength optical packet forwarding technology can de-risk network investments by 500% when compared to a next-generation IPoDWDM solution. Second, in a scaled network scenario the sub-wavelength solution also provides 150% capital savings. Finally, on the medium and long term the sub-wavelength approach yields a cost benefit for 99.8% of the configurations, when compared to an IPoDWDM architecture.