A general framework for supporting economic feasibility of generator and storage energy systems through capacity and dispatch optimization

Abstract

Integration of various electricity-generating technologies (such as natural gas, wind, nuclear, etc.) with storage systems (such as thermal, battery electric, hydrogen, etc.) has the potential to improve the economic competitiveness of modern energy systems. Driven by the need to efficiently assess the economic feasibility of various energy system configurations in early system concept development, this work outlines a versatile computational framework for assessing the net present value of various integrated storage technologies. The subsystems' fundamental dynamics are defined, with a particular emphasis on balancing critical physical and economic domains to enable optimal decision-making in the context of capacity and dispatch optimization. In its presented form, the framework formulates a linear, convex optimization problem that can be efficiently solved using a direct transcription approach in the open-source software DTQP. Three case studies demonstrate and validate the framework's capabilities, highlighting its value and computational efficiency in facilitating the economic assessment of various energy system configurations. In particular, natural gas with thermal storage and carbon capture, wind energy with battery storage, and nuclear with hydrogen are demonstrated.

Figures (14)

• Figure 1: Illustration of an HES architecture with a collection of homogeneous electric power generators and three types of storage systems. Primary (such as thermal), electrical (such as battery), and tertiary (such as hydrogen) storage is shown in red, green, and blue, respectively. The charge and discharge signals, along with their associated efficiencies, are described by $\overrightharpoon{\mathord{\color{black!33}\bullet}}$ and $\overleftharpoon{\mathord{\color{black!33}\bullet}}$, respectively.
• Figure 2: HES candidate for Case Study i@: a natural gas combined cycle power plant with thermal storage and a carbon capture and storage system, Case Study ii@: a wind farm with a battery energy storage system, and Case Study iii@: a nuclear power plant with a hydrogen production (through high-temperature steam electrolysis) and storage facility.
• Figure 3: Case Study I: Optimal state and control variables for combined cycle generator with thermal storage and carbon capture. Here $x_{G}$ and $x_{S}$ are generator and storage states, respectively; $\overrightharpoon{u}_{\space}$, and $\overleftharpoon{u}_{\space}$ are charge and discharge control signals; $C_E$ and $C_{\text{fuel}}$ are electricity and fuel price signals.
• Figure 4: Case Study I: Optimal generator state ($x_{G}$) with electricity and fuel prices ($C_E$, and $C_{\text{fuel}}$) over a long horizon of around 42 days.
• Figure 5: Breakdown of various elements in Case Study i@, a combined cycle with thermal storage and carbon capture: (a) Generator energy usage, (b) Primary load contributions, and (c) Revenue contributions.