Abstract
In the context of decarbonization, the integration of a substantial volume of low-carbon technologies into the energy network presents both opportunities and challenges. This shift aims to create a low-cost, low-carbon energy system that benefits operators, consumers, and the environment. The transition to a future of lower carbon energy requires fundamental changes in network operations and investments, underscoring the importance of appropriate investment decisions and incentives. A pricing method that reflects the location and size of low-carbon technologies can inform these investment decisions effectively.Current and previous uncertainty mechanisms primarily serve as post-adjustment tools which do not involve the signals of initial investment guidance for companies. Many extensively researched network pricing methods are either restricted to assigning the existing network to current customers without actively attempting to lower future network investment costs or lack the consideration of future load growth changes in the low-carbon energy environment under long-term uncertainties. This oversight can lead to network constraints due to insufficient investment or excessive investment that fails to achieve its intended goal and incurs higher costs for consumers. Furthermore, the advancement of low-carbon technologies introduces dynamics in load growth rates, a factor often overlooked in existing pricing methods.
Therefore, the principal achievements of the thesis lie in the field of uncertainties by demand decomposition and enhanced incentive mechanisms in view of low-carbon technologies. To tackle these challenges, this thesis aims at:
• Implementing long-term demand forecasting based on demand decomposition to uncover fundamental demand trends, thereby mitigating investment risks regarding network reinforcement.
• Developing incentive mechanisms to inform and regulate investment in the uncertain conditions, minimizing the potential for over- or under-investment by DNOs and improving regulatory oversight of DNO investment actions.
• Developing a novel pricing method that is adaptive to the decarbonizing environment, promoting pricing stability while preserving cost-reflective.
The principal contributions of this thesis are as follows:
• A network pricing method based on demand decomposition is introduced, incorporating Long-run incremental cost pricing. Traditional distribution network pricing heavily depends on load forecasts. To address the uncertainties in load growth rate due to the advancement of LCTs, the proposed method utilizes the moving average to extract the trend-cycle from raw data and historical peak demands, producing a smoother curve while maintaining the cost-reflectivity of conventional methods. This method yields more predictable pricing for newcomers to the network, lowering investment risks and supporting informed decision-making.
• A revenue adjustment incentive mechanism is developed to inform investment decisions by DNOs, integrating capacity volume driver to account for LCT growth and employing the mechanism of maximum utilization to assess network utilization level for future investments. Current methods often fail to reconcile the cost-benefit imbalance DNOs face within the condition of uncertain load growth. The proposed method helps network operators form a more realistic forecast of future investments by providing a clear view of network status post-EV connection.
• A dual-rate reward-penalty mechanism is proposed to regulate DNO investment actions. This mechanism offers variable caps for rewards and penalties, enhancing flexibility and adaptability under different scenarios, which marks a significant improvement over traditional single-rate mechanisms.
• A novel pricing method that accounts for both LCT demand and generation is proposed, incorporating the mechanism of optimal utilization. Existing pricing models typically concentrate on traditional load variations and neglect the rise in LCT adoption. By factoring in EV stock growth and implementing differentiated pricing for regular and peak PV generation periods, the proposed method not only reduces costs but also ensures pricing stability, thus promoting efficient and sustainable network operations.
These concepts are demonstrated on practical distribution systems taken from the network of Bath, UK and compared with the original model in terms of cost-reflectivity and flexibility under uncertainties. The presented outcomes validate the effectiveness of the proposed model and the advantages in influencing network investment with consideration of the development of low-carbon technology and reducing the needed investment.
| Date of Award | 11 Dec 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Chenghong Gu (Supervisor) & Furong Li (Supervisor) |