Regulated generation allocation and operation optimization for networks with new variable independent power production and self-generation

Student thesis: Doctoral Thesis

Abstract

The world is moving towards legally binding targets for decarbonisation, with considerable interest in cost effective energy pathways that will have positive socio-economic, environmental and health impacts. The electricity sector is progressing by adopting renewable energy as a replacement for fossil fuel-based electricity generation. As renewable energy sources (RES) in the form of independent power production (IPP) and on-site or self-generation (SG) proliferate on power networks, questions arise about their impact on the financial integrity of the traditional power distribution business. As distribution companies (DISCOs) act to protect their own financial interests, network access barriers will be presented to emerging RES. Network regulation is expected to drive DISCOs to pursue a more socially desirable outcome.However, today’s methods of network regulation are not adequate enough to remove the barriers and still ensure renewable energy goals are met. In fact there are no widely-accepted and clear mechanisms to encourage DISCOs to coordinate distributed generation, let alone SG and IPP, integration in a cost-effective manner. In terms of policy, companies can be obligated to meet a quota of RES in their energy supply. But this obligation is usually not guaranteed to align with the capabilities of power networks, which typically suffer from voltage and congestion constraints among others. To set achievable quotas there is a need for a more adaptable mechanism that takes into account capacity constraints.The work of this thesis concerns the formulation and empirical analyses of optimisation models of structured RES allocation by a regulated DISCO, and the regulating authority’s role in influencing the DISCO’s planning approach and promoting socially desirable performance. The developed optimisation models uniquely: introduce combined SG and IPP allocation, which allows generation to be defined in association with on-site demand; provide generation capacity that simultaneously meets network, policy and regulatory requirements (i.e. there is no need to individually evaluate the same implications from the calculated capacity); take account of generation curtailment and its underlying restrictions for SG and IPP; demonstrate SG and IPP allocations for range of quota obligations; and benchmark the performance of the models against alternative approaches of generation allocation and regulation.This results in a problem with a multilevel structure necessitating the computation of spatial capacity and a solution to the multi-period optimal power flow. The problem variables further depend on the perspective of stakeholders in the electricity market. From the viewpoint of the DISCO, the solution intends to provide suitably sited DG capacity and maximise profit. As for the regulating authority the results offer the most suitable reward or penalty to drive the DISCO towards a low carbon network.In response, the regulated DISCO should then carry out DG planning in line with broader goals of society. This joint SG and IPP integration problem lends itself specific and unique constraints including generation class-specific net generation and energy curtailment.The results reported in this thesis highlight the value and performance of the DISCO and regulation optimisation models on several power networks of varying size and composition. Numerical experiments demonstrate the developed DISCO optimisation model outperforms standard models, concerned primarily with capacity maximisation, in satisfying the following binding constraints: minimum IPP capacity and SG net energy.It is further revealed that integrating SG and IPP in a benchmark system with the proposed model increases profit by up to 23.7%, adding an improvement of 8% over a feasible standard model. In a case study of a network with extremely limited capacity—insufficient for
Date of Award13 Mar 2020
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorStuart Galloway (Supervisor) & Graeme Burt (Supervisor)

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