Flexible Distributed Multienergy Generation System Expansion Planning Under Uncertainty

TLDR

Smart grids leverage demand‑side resources to increase efficiency, and multienergy systems that jointly optimize gas, electricity, and heat offer valuable flexibility, yet their planning is difficult under long‑term price uncertainty. This paper introduces a unified operation‑and‑planning optimization framework for distributed multienergy generation systems to evaluate flexibility in both operational and investment stages amid long‑term uncertainties. The framework adopts real‑options thinking, formulated as a stochastic mixed‑integer linear program, and is demonstrated on a realistic UK district‑energy case study featuring a CHP plant, electric heat pumps, and thermal storage. Results indicate that the proposed method reduces expected cost and risk compared to less flexible planning approaches, thereby strengthening the business case for flexible DMG systems.

Abstract

A key feature of smart grids is the use of demand side resources to provide flexibility to the energy system and thus increase its efficiency. Multienergy systems where different energy vectors such as gas, electricity, and heat are optimized simultaneously prove to be a valuable source of demand side flexibility. However, planning of such systems may be extremely challenging, particularly in the presence of long-term price uncertainty in the underlying energy vectors. In this light, this paper proposes a unified operation and planning optimization methodology for distributed multienergy generation (DMG) systems with the aim of assessing flexibility embedded in both operation and investment stages subject to long-term uncertainties. The proposed approach reflects real options thinking borrowed from finance, and is cast as a stochastic mixed integer linear program. The methodology is illustrated through a realistic U.K.-based DMG case study for district energy systems, with combined heat and power plant, electric heat pumps, and thermal energy storage. The results show that the proposed approach allows reduction in both expected cost and risk relative to other less flexible planning methods, thus potentially enhancing the business case of flexible DMG systems.