A Graph Theory-Based Method for Regional Integrated Energy Network Planning: A Case Study of a China–U.S. Low-Carbon Demonstration City

Chen, Yingying; Zhu, Jian

doi:10.3390/en12234491

Cited by 11 publications

(1 citation statement)

References 18 publications

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“…Regional energy planning is an integrated management of energy generation and consumption based on the economic development, environmental, and social resources within an area (Shah et al 2020). Regional energy planning models are typically developed via mathematical programming (MP), which specify the optimization of a specific function (i.e., minimizing emissions and maximizing efficiency) subject to constraints that reflect real-life conditions in an algebraic form (Chen and Zhu 2019). Solving the MP would then produce a result that can be interpreted as the optimal design of an energy system (Akbas et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Regional Planning and Optimization of Renewable Energy Sources for Improved Rural Electrification

Shahrom

Aviso

Tan

et al. 2023

Process Integr Optim Sustain

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Over 13% of the global population (most of which are rural communities) still lack access to electricity. A typical resolution to this would be to generate more electricity from existing power generation infrastructure. However, the urgency to meet net-zero global greenhouse gas emissions means that this resolution may not be the way forward. Instead, policymakers must consider decarbonization strategies such as renewable energy systems to generate more electricity in rural communities. As policymakers aim to encourage renewable energy generation, existing power plant operators may not share the same perspective. Operators typically wish to ensure profit margins in their operations as decarbonization efforts may be costly and reduce the profit. A balance must be struck between both parties so that the energy sector can continue to meet rising energy demands and decarbonization needs. This is a classic leader–follower situation where it involves the interplay between policymaker (as energy sector regulator) and industry (as energy sector investor). This work presents a bi-level optimization model to address the leader–follower interactions between policymakers and industry operators. The proposed model considers factors such as total investment, co-firing opportunities, incentives, disincentives, carbon emissions, scale, cost, and efficiency to meet electricity demands. To demonstrate the model, two Malaysian case studies were evaluated and presented. The first optimized networks is developed based on different energy demands. Results showed that when cost was minimized, the production capacity of the existing power plants was increased and renewable energy systems were not be selected. The second case study used bi-level optimization to determine an optimal trade-off $ 1.4 million in incentives per year, which serves as a monetary sum needed by policymakers to encourage industry operators to decarbonize their operations. Results from the second case were then compared to the ones in the first.

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Section: Introductionmentioning

confidence: 99%