System-Wide Emergency Policy for Transitioning from Main to Secondary Fuel

Abstract

Faced with the complexities of managing natural gas-dependent power system amid the surge of renewable integration and load unpredictability, this study explores strategies for navigating emergency transitions to costlier secondary fuels. Our aim is to develop decision-support tools for operators during such exigencies. We approach the problem through a Markov Decision Process (MDP) framework, accounting for multiple uncertainties. These include the potential for dual-fuel generator failures and operator response during high-pressure situations. Additionally, we consider the finite reserves of primary fuel, governed by gas-flow partial differential equations (PDEs) and constrained by nodal pressure. Other factors include the variability in power forecasts due to renewable generation and the economic impact of compulsory load shedding. For tractability, we address the MDP in a simplified context, replacing it by Markov Processes evaluated against a selection of policies and scenarios for comparison. Our study considers two models for the natural gas system: an over-simplified model tracking linepack and a more nuanced model that accounts for gas flow network heterogeneity. The efficacy of our methods is demonstrated using a realistic model replicating Israel's power-gas infrastructure.

Figures (8)

• Figure 1: Diagram illustrating the relationships between system operator actions affecting generator status, the resulting states of the generators and gas systems, and the corresponding observations.
• Figure 2: The Markov Process describes transitions between the operational states of a generator, which can be in one of three main states: Main Fuel (MF), Secondary Fuel (SF), and Offline (OFF). Additionally, there are transient states, e.g., (OM) and (OS), which represent transitions between OFF and MF, and between OFF and SF, respectively. These transient states consist of multiple sub-states (not shown), with the number of sub-states determined to match the expected transition duration based on the time step, $\Delta t$. The sub-script XX corresponds to the transient states (MS, SM, OS, or OM). States are color-coded based on their energy production status: green and blue states consume gas or diesel respectively. Actions are similarly color-coded: grey indicates no action, yellow represents a transition, purple and cyan denote start-up using main and secondary fuels respectively, and red represents shutdown. Shutdowns are considered instantaneous and reliable, with no transition state associated with them.
• Figure 3: Map of the Israeli gas system and list of units. Gas platforms denote the two injection points, which are lost at the start of the emergency scenario. The three regions used in Fig. \ref{['fig:comparisons']}: North (green), Center (red) and South (blue).
• Figure 4: Scenario with super reliable units and $K$ is set to 1 (column a) and to 10 (column b) and the reserve is $R=500$MW.
• Figure 5: Scenario with fairly reliable units and max number of actions $K$ is set to $5$ and with no reserve capacity $R=0$MW in (a) and with a reserve capacity $R=1000$MW in (b). The bottom plot shows the status of the fleet: the number of generators on main fuel, on secondary fuel, on transition, and offline are displayed in blue, green, purple and red, respectively.