Unit Commitment With Gas Network Awareness

Abstract

Recent changes in the fuel mix for electricity generation and, in particular, the increase in Gas-Fueled Power Plants (GFPP), have created significant interdependencies between the electrical power and natural gas transmission systems. However, despite their physical and economic couplings, these networks are still operated independently, with asynchronous market mechanisms. This mode of operation may lead to significant economic and reliability risks in congested environments as revealed by the 2014 polar vortex event experienced by the northeastern United States. To mitigate these risks, while preserving the current structure of the markets, this paper explores the idea of introducing gas network awareness into the standard unit commitment model. Under the assumption that the power system operator has some (or full) knowledge of gas demand forecast and the gas network, the paper proposes a tri-level mathematical program where natural gas zonal prices are given by the dual solutions of natural-gas flux conservation constraints and commitment decisions are subject to bid-validity constraints that ensure the economic viability of the committed GFPPs. This tri-level program can be reformulated as a single-level Mixed-Integer Second-Order Cone program which can then be solved using a dedicated Benders decomposition. The approach is validated on a case study for the Northeastern United States [1] that can reproduce the gas and electricity price spikes experienced during the early winter of 2014. The results on the case study demonstrate that gas awareness in unit commitment is instrumental in avoiding the peaks in electricity prices while keeping the gas prices to reasonable levels.

Two-Layer Optimization Scheduling Model of Integrated Electricity and Natural Gas Energy System Considering the Feasibility of Gas-Fired Units’ Reserve

“…In references [4]- [7], the planning of IEGS is analyzed. References [8], [9] focus on the electricity markets and natural gas markets of IEGS. The long-term integrated planning of interdependent natural gas and electric transmission systems is analyzed in [10].…”

Strategic-Agent Equilibria in the Operation of Natural Gas and Power Markets

“…However, tools to comprehend the effect of such interdependency are limited. Many of these tools adopt a centralized perspective, in which a single operator manages both the gas and power systems [3][4][5][6][7][8][9][10][11][12][13][14] , which is unrealistic. Representative references are briefly discussed below.…”

“…Chen et al [3] develop a unit commitment model that includes an enhanced second order conic gas flow model, where the interdependency between gas and power prices is investigated. Byeon and Van Hentenryck [4] introduce a unit commitment problem with gas network awareness, where bid-validity constraints are imposed on gas-fired units. He et al [5] propose an integrated gas and power system operation model that considers demand response and uncertainty via distributionally robust optimization.…”

A Stochastic Two-Stage Model for the Integrated Scheduling of the Electric and Natural Gas Systems

“…In the literature there is a clear differentiation between: (a) the coordinated operation of the power and gas systems [4]- [8], namely the separate scheduling and operation of each system, but with a -usually iterative-coordination process during which the technical infeasibilities of one problem are passed as binding constraints to the other problem until all infeasibilities are gradually resolved at the end of the coordination process, and (b) the integrated co-optimization (scheduling and operation) of the two systems [9]- [14], namely the solution of a single problem targeting at the minimization of the overall cost of both systems, respecting the technical constraints of both systems, and being solved at one-shot for the optimal combined solution. Apparently, the theoretically optimal solution is the second one, leading to the overall optimal solution.…”

“…There is a rich literature on deterministic formulations [4]- [14]; an extensive and thorough discussion on recent research works is provided in [15]. Recently, more sophisticated approaches have been presented with novel linearization algorithms, as per second order cone relaxations in [8] and [14]. However, the deterministic approaches may fail to incorporate the inherent stochasticity of several parameters affecting the day-ahead power and gas scheduling solution, such as the level of the electricity load, the renewable injections (especially of wind and PV stations) and the gas load from residential, commercial and industrial consumers.…”