Optimizing Economic Feasibility of Integrated Generator and Storage Energy Systems through Capacity and Dispatch Optimization

Core Concepts

A versatile computational framework for assessing the net present value of various integrated generator and storage energy systems through capacity and dispatch optimization.

Abstract

The article presents a computational framework called ECOGEN-CCD for assessing the economic feasibility of integrated generator and storage energy systems. The framework formulates a linear, convex optimization problem that can be efficiently solved using direct transcription in the open-source software DTQP. The framework considers an integrated energy system architecture with various generators (e.g., natural gas, wind, nuclear) and three types of storage systems (primary, electrical, and tertiary). The problem elements include plant variables (storage capacities), control variables (generator power, storage charge/discharge), and state variables (generator power level, storage energy levels). Constraints are defined for the storage capacities, generator and storage operation, and energy balance. The objective function is to maximize the net present value (NPV) by optimizing the capacity and dispatch of the integrated system. The framework accounts for various techno-economic considerations, including capital costs, operation and maintenance costs, fuel costs, carbon costs, and revenue from selling electricity, primary energy, and tertiary commodities. Three case studies are presented to demonstrate the capabilities of the framework: 1) a natural gas combined cycle power plant with thermal storage and carbon capture, 2) a wind farm with a battery energy storage system, and 3) a nuclear power plant with a hydrogen production and storage facility. The results show the optimal capacity and dispatch decisions that maximize the NPV for each case, highlighting the value and computational efficiency of the ECOGEN-CCD framework in facilitating the economic assessment of various integrated energy system configurations.

Stats

The capital cost of the natural gas combined cycle power plant is $958,000/MW. The capital cost of the wind farm is $1,265,000/MW. The capital cost of the nuclear power plant is $6,041,000/MW. The capital cost of the thermal energy storage system is $1,048,947/MWh. The capital cost of the battery energy storage system is $347,000/MWh. The capital cost of the hydrogen storage system is $600.074/kg.

Quotes

"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." "To be economically beneficial, the flexibility added by incorporating these generators in the context of integrated energy systems must be sufficient to overcome the capital and operational cost of the technology over its lifetime."

Key Insights Distilled From

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

by Saeed Azad,Z... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14583.pdf

Deeper Inquiries

How can the framework be extended to consider more advanced financial parameters, such as loan, depreciation, and other factors that impact the economic analysis

To extend the framework to consider more advanced financial parameters, such as loan and depreciation, several modifications and additions can be made. Loan Parameters: Including loan parameters in the economic analysis would involve incorporating factors such as interest rates, loan duration, and repayment schedules. This would require adjusting the cost calculations to account for the impact of loans on the overall project costs. The framework could be updated to calculate the total project costs considering the loan amount, interest rates, and repayment schedules. Depreciation: Depreciation is a crucial aspect of financial analysis, especially for long-term assets like energy systems. Including depreciation in the framework would involve accounting for the decrease in value of assets over time. This could be achieved by adjusting the capital costs and operating expenses to reflect the depreciation of assets over their useful life. Sensitivity Analysis: To account for the uncertainties associated with financial parameters, the framework could be enhanced to include sensitivity analysis. This would involve analyzing the impact of variations in financial parameters (such as interest rates, inflation rates, and loan terms) on the economic feasibility of the energy systems. By conducting sensitivity analysis, decision-makers can better understand the financial risks and uncertainties associated with the project. Integration with Financial Models: The framework could be integrated with financial modeling tools to provide a more comprehensive analysis of the economic feasibility of integrated energy systems. By linking the optimization framework with financial models, users can perform detailed financial analysis, including cash flow projections, net present value calculations, and internal rate of return analysis. By incorporating these enhancements, the framework can provide a more robust and comprehensive analysis of the economic feasibility of generator and storage energy systems, taking into account advanced financial parameters.

What are the potential limitations of the linear, convex optimization approach used in the framework, and how could non-linear models be incorporated to capture more detailed system dynamics

The linear, convex optimization approach used in the framework has certain limitations that may impact its ability to capture complex system dynamics. Some potential limitations include: Simplified Representation: Linear optimization models may oversimplify the complexities of real-world energy systems. Non-linear effects, such as ramping constraints, start-up costs, and non-linear efficiency curves, may not be accurately captured in a linear model. Limited Flexibility: Linear models are inherently rigid and may not allow for the representation of certain system behaviors that exhibit non-linear relationships. This lack of flexibility could limit the model's ability to capture the full range of possible system dynamics. Accuracy of Results: Linear models may provide approximate solutions that are not as precise as non-linear models. In cases where system dynamics are highly non-linear, the linear optimization approach may not yield accurate results. To incorporate more detailed system dynamics, non-linear models could be integrated into the framework. Non-linear optimization techniques, such as mixed-integer non-linear programming (MINLP) or non-linear programming (NLP), could be used to capture the intricacies of energy system operations more accurately. These models would allow for the inclusion of non-linear constraints, variable costs, and other complex dynamics that are not easily represented in linear models. By incorporating non-linear optimization techniques, the framework could provide more detailed and realistic assessments of the economic feasibility of integrated energy systems, capturing the nuances of system behavior more effectively.

How could the framework be adapted to consider the impact of future market trends, policy changes, and technological advancements on the economic feasibility of integrated energy systems

To adapt the framework to consider the impact of future market trends, policy changes, and technological advancements on the economic feasibility of integrated energy systems, the following strategies could be implemented: Scenario Analysis: The framework could be enhanced to include scenario analysis, where different future scenarios based on market trends, policy changes, and technological advancements are considered. By running the optimization model under various scenarios, decision-makers can assess the robustness of the energy systems to different future conditions. Dynamic Pricing Models: Incorporating dynamic pricing models that account for future market trends and policy changes could provide more realistic economic assessments. By simulating different pricing scenarios, the framework can evaluate the system's economic performance under varying market conditions. Policy Impact Assessment: The framework could be extended to include an analysis of the impact of policy changes on the economic feasibility of energy systems. By incorporating policy variables into the optimization model, decision-makers can evaluate how changes in regulations or incentives affect the financial viability of the systems. Technology Integration: Considering the impact of future technological advancements on the energy systems is crucial. The framework could be updated to include options for integrating new technologies, such as advanced storage systems or renewable energy sources, and assessing their economic implications. By incorporating these adaptations, the framework can provide a more comprehensive analysis of the economic feasibility of integrated energy systems, taking into account future market trends, policy changes, and technological advancements.