• Energy Research

The blending of hydrogen generated using clean energy into natural gas pipeline networks is proposed in order to utilize existing energy systems for their planned lifetimes while reducing their reliance on fossil fuels. We formulate a system of partial differential equations (PDEs) that govern the flow dynamics of mixtures of gases in pipeline networks under the influence of time-varying compressor and regulator control actions. The formulation is derived for general gas networks that can inject or withdraw arbitrary time-varying mixtures of gases into or from the network at arbitrarily specified nodes. The PDE formulation is discretized in space to form a nonlinear control system that is used to prove that homogeneous mixtures are well-behaved and heterogeneous mixtures may be ill-behaved in the sense of monotone-ordering of solutions. We use numerical simulations to compute interfaces in the parameter region of sinusoidal boundary conditions that delimit monotonic, periodic, and chaotic system responses. The interfaces suggest that any solution in the monotonic response region is not chaotic and will eventually approach a periodic orbit. The results are demonstrated using examples for a single pipeline and a small test network.

We outline a new control system model for the distributed dynamics of compressible gas flow through large-scale pipeline networks with time-varying injections, withdrawals, and control actions of compressors and regulators. The gas dynamics PDE equations over the pipelines, together with boundary conditions at junctions, are reduced using lumped elements to a sparse nonlinear ODE system expressed in vector-matrix form using graph theoretic notation. This system, which we call the reduced network flow (RNF) model, is a consistent discretization of the PDE equations for gas flow. The RNF forms the dynamic constraints for optimal control problems for pipeline systems with known time-varying withdrawals and injections and gas pressure limits throughout the network. The objectives include economic transient compression (ETC) and minimum load shedding (MLS), which involve minimizing compression costs or, if that is infeasible, minimizing the unfulfilled deliveries, respectively. These continuous functional optimization problems are approximated using the Legendre-Gauss-Lobatto (LGL) pseudospectral collocation scheme to yield a family of nonlinear programs, whose solutions approach the optima with finer discretization. Simulation and optimization of time-varying scenarios on an example natural gas transmission network demonstrate the gains in security and efficiency over methods that assume steady-state behavior.

The abundance of natural gas in the United States and the need for cleaner electric power have prompted widespread installation of gas-fired power plants and caused electric power systems to depend heavily on reliable gas supplies. The use of gas generators for peak load and reserve generation causes high intra-day variability in withdrawals from high pressure gas transmission systems, which leads to gas price fluctuations and supply disruptions that affect electric generator dispatch and threaten the security of both power and gas systems. In this manuscript, we investigate different gas compressor operation policies and their influence on the affected power system. Specifically, we consider constant pressure boost ratios and dynamic adjustment of these ratios to track pressure set-points. We also implement a joint optimization of generator dispatch schedules and gas compressor protocols using a dynamic gas flow model. We develop tractable, physically accurate implementations that are compared using an integrated model of test networks for power and gas systems with 24 and 25 nodes, which are coupled through gas-fired generators. This demonstrates the benefits that can be achieved with globally optimized gas system operations and increased gas-electric coordination.

We derive a reduced control system model for the dynamics of compressible gas flow through a pipeline subject to distributed time-varying injections, withdrawals, and control actions of compressors. The gas dynamics PDE equations are simplified using lumped elements to a nonlinear ODE system with matrix coefficients. We verify that low-order integration of this ODE system with adaptive time-stepping is computationally consistent with solution of the PDE system using a split-step characteristic scheme on a regular space-time grid for a realistic pipeline model. Furthermore, the reduced model is tractable for use as the dynamic constraints of the optimal control problem of minimizing compression costs given transient withdrawals and gas pressure constraints. We discretize this problem as a finite nonlinear program using a pseudospectral collocation scheme, which we solve to obtain a polynomial approximation of the optimal transient compression controls. The method is applied to an example involving the Williams-Transco pipeline.

Increasing use of natural gas for electricity production places added strains on pipeline systems that are used for transporting fuel. Pipeline constraints require power system operators to account for natural gas-supply restrictions in their operational processes. This paper proposes separate optimization models for clearing day-ahead wholesale markets for scheduling power and natural gas systems. We then develop a market-based mechanism that allows for efficient co-ordination of the two systems. Importantly, the co-ordination mechanism only requires the exchange of fuel-price, -supply, and -demand information between the two markets. This can be contrasted with other co-ordination mechanisms that require operations of the two systems by a single entity. Thus, we provide a computationally tractable co-ordination mechanism that does not require the exchange of proprietary information between natural gas and electricity system operators. We demonstrate the effectiveness and scalability of the technique using a numerical example.

We formulate two estimation problems for pipeline systems in which measurements of compressible gas flow through a network of pipes is affected by time-varying injections, withdrawals, and compression. We consider a state estimation problem that is then extended to a joint state and parameter estimation problem that can be used for data assimilation. In both formulations, the flow dynamics are described on each pipe by space- and time-dependent density and mass flux that evolve according to a system of coupled partial differential equations, in which momentum dissipation is modelled using the Darcy-Wiesbach friction approximation. These dynamics are first spatially discretized to obtain a system of nonlinear ordinary differential equations on which state and parameter estimation formulations are given as nonlinear least squares problems. A rapid, scalable computational method for performing a nonlinear least squares estimation is developed. Extensive simulations and computational experiments on multiple pipeline test networks demonstrate the effectiveness of the formulations in obtaining state and parameter estimates in the presence of measurement and process noise. Published in IEEE Transactions on Control Systems Technology

AbstractThe majority of overland transport needs for crude petroleum and refined petroleum products are met using pipelines. Numerous studies have developed optimization methods for design of these systems in order to minimize construction costs while meeting capacity requirements. Here, we formulate problems to optimize the operations of existing single liquid commodity pipeline systems subject to physical flow and pump engineering constraints. The objectives are to maximize the economic value created for users of the system and to minimize operating costs. We present a general computational method for this class of continuous, non‐convex nonlinear programs, and examine the use of pump operating settings and flow allocations as decision variables. The approach is applied to compute optimal operating regimes and perform engineering economic sensitivity analyses for a case study of a crude oil pipeline developed using publicly available data.

We develop an explicit second order staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge domain boundary values that are significantly more complex than in the case of a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.