Use of Ternary Optimization in the Integrated Energy Systems

Abstract

The rapid development of Integrated Energy Systems (IES), which unify diverse energy technologies such as electricity, heat, cooling, and gas, has heightened the importance of optimizing their operational modes. This paper explores the application of ternary optimization in IES, a discrete optimization approach where variables are constrained to three values: {-1, 0, +1}. Ternary optimization offers a balanced trade-off between binary and full-precision optimization, providing significant advantages in computational efficiency, memory savings, and energy efficiency. The article covers: key concepts of ternary optimization, including ternary representation, sparsity, and quantization; advantages and challenges of ternary optimization, such as reducing computational complexity and potential loss of accuracy; the application of ternary optimization for the IES. The role of ternary optimization in simplifying energy flow management, reducing computational resources, and enabling faster decision-making in dynamic environments is emphasized. Examples of using ternary optimization for energy distribution, microgrid management, integration of renewable energy sources, and energy storage systems are provided. A practical example of transforming an optimization model for IES into a ternary model using GMPL (GNU MathProg Language) is provided, demonstrating how ternary variables, constraints, and objective functions can be adapted. The paper concludes by discussing promising directions for ternary optimization in IES, including integration with AI and machine learning, development of specialized algorithms, and hardware support for ternary computations. Research underscores the potential of ternary optimization to enhance the efficiency, resilience, and scalability of IES, particularly in the context of increasing renewable energy integration and the complexity of modern energy grids.