Tomislav Capuder

Changes in the electricity business environment, dictated mostly by the increasing integration of renewable energy sources characterised by variable and uncertain generation, create new challenges especially in the liberalised market environment. The role of energy storage systems (ESS) is recognised as a mean to provide additional system security, reliability and flexibility to respond to changes that are still difficult to accurately forecast. However, there are still open questions about benefits these units bring to the generation side, system operators and the consumers. This study provides a comprehensive overview of the current research on ESS allocation (ESS sizing and siting), giving a unique insight into issues and challenges of integrating ESS into distribution networks and thus giving framework guidelines for future ESS research.

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.