Abstract
The modern electrical power industry is undergoing substantial changes due to major growth in low-carbon technologies (LCTs) for global de-carbonization. Local Energy Markets (LEMs) are designed to coordinate those Distributed Resources (DERs) efficiently and economically for larger and more complex Distribution Systems. New market functions and products in LEMs, such as customer flexibility trading, provide more options for Distribution Network Operators (DNOs) to manage the distribution system more actively and efficiently. LEM allows energy customers and network operators to contribute to decarbonisation.Most market-signal-driven LEM designs in distribution networks focus either on reducing distribution network utilisation peaks to delay network investments or on shifting customer demands to align with the output of renewable generation patterns for greater decarbonisation. Flexible demands can be incentivised to consume cheaper energy, which could potentially result in higher network utilisation peaks and network congestion. Therefore, there is a need for LEMs to provide market signals that incorporate both the degree of network utilisation and distributed energy consumption, incentivising flexible energy customers to perform Demand-Side Responses (DSRs) to balance the benefits of renewable energy producers and network asset owners.
The key contribution of this thesis is to develop a two-dimensional LEM that strikes the balance of the benefits delivered by increasing renewable energy consumption and by delaying network investments. Each LEM in this thesis includes two sub-markets: the energy market and the network market. The energy market communicates the energy prices from different resources while the network market determines the Distribution Use of System (DUoS) charges. Different levels of LCT penetrations will lead to various energy and/or network problems, indicating that there is no one-fit-to-all solution to LEM design. The market arrangements (e.g., only energy-only/network-only markets and combined energy-and-network markets) can vary across distribution networks with different LCT penetration levels, loading levels, and network capacities, to address the emerging energy/network problems and balance the benefits of market participants. In the distribution network without local energy resources, DUoS charges are based on Long Run Incremental Cost (LRIC). However, in the distribution network with local energy resources and network congestions, Congestion Revenues are used to evaluate network asset value. Congestion Revenues reflect the asset values derived from energy transportation, playing key roles in network pricing approaches and network planning/investment. Stochastic components are used in Congestion Revenue calculations to adapt to the networks with incomplete demand data and uncertain growths of LCTs.
To improve the understanding of LEM design, this thesis conducts network problem identifications, benefits analysis, and introduces DUoS charge into LEM signals and develops a stochastic approach for Congestion Revenues. The key innovations and contributions of this thesis include:
1)Proposes a two-dimensional LEM framework where the market signals are influenced by the energy prices and network status, thereby providing a market-driven load regulation to the network; 2) Investigates how DER values are conveyed by different market arrangements (with/without DUoS charges in LEM signals) in the distribution networks; 3) Identifies how the problems in distribution networks can transition due to changes in network conditions and market arrangements; 4) Demonstrates the key drivers of DER values.
1)Proposes an enhanced network pricing strategy, the resulting dynamic network charges of which can guide the load pattern to balance the benefits from both network peak reduction and renewable energy consumption; 2) Demonstrates how the proposed network pricing strategy should adapt when the network conditions change.
1)Proposes a novel stochastic approach to investigate the value-in-use of network assets under the influence of incomplete data in the distribution network; 2)Validates the proposed approach by comparing its results with those obtained from the classical approach; 3) Demonstrates the potential for error adjustment in the proposed approach when using incomplete data; 4)Estimates the impacts of LCTs on network asset values with limited information from integrated LCTs.
| Date of Award | 26 Jun 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Furong Li (Supervisor), Ran Li (Supervisor) & Kang Ma (Supervisor) |