Network Pricing for Smart Grid under Uncertain Future Energy Landscape
: (Alternative Format Thesis)

  • Xinhe Yang

Student thesis: Doctoral ThesisPhD


The power system has been experiencing a rapid change over the past decade since low-carbon emission became a prevailing environmental policy for most countries in the world. As a leading country, the UK has reduced its greenhouse gas emissions by 51% by 2020 compared to 1990 levels and committed to a net-zero carbon emission target by 2050. This profoundly changes how energy is produced, transmitted and consumed. The large-scale centralised generations connected to transmission network is shifted to small scale renewable generations connected to distribution network. Meanwhile, emerging advanced load techniques such as EV, storage and smart appliances enable end-users to transit from passive energy takers to active system participants. The power system is heading to a decarbonised, decentralised and digitalised future. However, this highly dynamic power system brings greater uncertainty and complexity for the system operator to manage their networks, which requires a refresh thinking on how networks will be operated and invested.
In the UK, the network charge is to recover the network cost of utilities and ensure their profits in providing power transmission services from generators to end demand. With an appropriate design, the network charge could 1) guide the optimal resource allocation, including distributed energy resources (DERs), low-carbon technologies (LCTs) and network investments; and 2) guide the users’ behaviours in utilising network infrastructures. The traditional deterministic-based network pricing method does not fit for purpose to precisely capture the impact of uncertainties and reflect the new characteristics of network users in a highly dynamic power system. Thus, the network pricing method needs further development to ensure its effectiveness in providing cost-reflective, forward-looking and fair network charges to network users, which facilitates a more efficiently utilised network and cleaner power system at a lower cost.
To tackle these new challenges, this research:
• Identifies and models key uncertain factors affecting the network pricing design.
• Evaluates impacts of uncertainties on network investment planning, network cost determination and network cost allocation.
• Designs novel network pricing methods to directly incentivise network users to reduce uncertainties on consumption, reducing users’ bills and network cost.
• Designs network pricing methods to indirectly incentivise the adopting of non-investment solutions, mitigating the risk of network companies under uncertainties.
The innovation and contribution of this research include:
• Developed a novel probabilistic based network pricing method for demand uncertainty in distribution network combining Long run incremental pricing. The existing network charge is determined by measuring network utilisation in a deterministic approach where demand is treated as constant input in the load flow modelling. The proposed pricing method considers the uncertainty of demand as an additional load attribute in pricing by using the probabilistic power flow technique, generating pricing signals based on diverse contributions of different loads on network utilisation. This provides an economic incentive for network users to reduce their uncertainties.
• Developed a reliability-based probabilistic network pricing method to compute nodal network charges considering the uncertainty of nodal demand. The improved probabilistic power flow modelling is applicable to large systems. The pricing signal to demand depends on both uncertainty levels of users and network reliability conditions. This provides an economic signal for demand users to guide their sitting and sizing while incentivising demand uncertainty reduction.
• Developed a novel network pricing method for flexible demand under the long-term load growth uncertainty. The emerging demand-side flexibility provides system operators with an alternative to stress the network capacity needs and defer the reinforcements. But traditional network pricing lacks sufficient signal to value the benefit from operation and connecting flexible demand. This method generates appropriate network charge signal according to the location and availability of flexible demand, promoting the utilisation and connection of flexible demand.
• Developed a novel decision-making tool for network operators and regulators to determine the planning horizon of the network under the uncertain future load growth. Traditional long-term network planning and large-scale investment induce a greater risk of asset stranding under the load growth uncertainty. This method promotes the shorter-term network planning and strategic network investment to secure the economic efficiency of network investment under uncertainty, reducing both the investment risk of network operators and the network charge for network users.
This work can help system operators to make appropriate investment plans under both long-term and short-term uncertainties and allocate the cost to users with diverse uncertainty and flexibility attributes, providing more cost-reflective and fair network pricing methods to direct the connection and behaviours of users.
Date of Award13 Dec 2021
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorChenghong Gu (Supervisor) & Furong Li (Supervisor)


  • network pricing
  • uncertainty modelling

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