| Summary: | Most electricity worldwide is supplied from the established centralized energy generation (CEG) system network which mainly operates using fossil fuels. An alternative decentralized energy generation (DEG) system has emerged with the advantage of generating electricity from locally available resources (usually renewable energy) for local consumption. DEG systems could avoid significant power losses during transmission in the CEG network and reduce the reliance on fossil fuels. These DEGs however, are geographically scattered and their resources are intermittent. One notable problem is that, at one point of time, some DEGs may have excess electricity and some may have electricity deficits; depending on the resource availability and the electricity consumption pattern. Weather-depending resources such as solar and wind energy could also affect the system's reliability. The energy gaps between one DEG to another can be solved, provided that the DEGs are integrated at the distribution level whereas the reliability issue can be overcome by integrating multiple DEGs to the existing CEG (which has a more stable electricity supply) at the transmission level. To deploy this complex integrated energy system, key decision parameters such as selection of technologies and their capacities, interactions between different units, overall system efficiency and costing at their optimum level have to be determined. There are limited studies in the literature regarding the wide-scale integration of DEGs with CEG and a lack of comprehensive optimization approach to solve for the system's design and scheduling. To fill these gaps, this research aimed to develop a novel targeting and optimization methodology for the design and scheduling of the DEG-CEG integrated energy system. A new numerical DEG-CEG integration framework was developed based on two enhanced Power Pinch approaches: (i) Extended Power Pinch Analysis for on-grid DEG system, and (ii) Extended Electrical Power System Cascade Analysis for CEG system with generation flexibility. The numerical framework optimized only the system's energy efficiency. A mixed integer nonlinear programming (MINLP) model was then developed to study the DEG-CEG system more holistically in terms of energy efficiency and costing, as well as to validate the optimal solutions resulted from the numerical framework. Both approaches were demonstrated using a hypothetical case study - an integrated energy system with multiple DEGs (operating using solar, wind and biomass energy) at different locations connected to one CEG (operating using natural gas) to fulfil power demand from residential, commercial and industrial sectors. From energy-efficient aspect, the numerical framework resulted in the system operating at an efficiency of 77 %, while the MINLP model showed 80.7 %. The difference of 3.7 % confirms the relevance of the numerical DEG-CEG integration framework as a systematic and effective energy planning tool in solving the design and scheduling problems of a power system. In term of costing, the MINLP model revealed that the system can achieve 77 % with a total cost of RM 936 million/y. Nevertheless, the numerical method is still an important analytical tool as the analysis provides visual insights that can be easily understood and appreciated by users like energy engineers and policymakers.
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