Litcius/Paper detail

CVR and Loss Optimization Through Active Voltage Management: A Trade-off Analysis

Hani Gharavi, Luis F. Ochoa, Xueqin Liu, Geraldine Paterson, Ben Ingham, Seán McLoone

2020IEEE Transactions on Power Delivery25 citationsDOIOpen Access PDF

Abstract

Distribution companies are increasingly turning to active network management to improve how networks perform with respect to different metrics or objectives. This includes Conservation Voltage Reduction (CVR) that seeks to minimize energy usage of consumers (mostly connected to low voltage, LV, levels) by reducing their supply voltage. However, if medium voltage (MV) levels have to be reduced to achieve CVR, it can negatively affect the energy losses in MV networks. In this study, we explore the potential trade-off that may exist between reducing energy consumption of LV networks and the potential increase in energy losses in MV networks. A Pareto Particle Swarm Optimization (PPSO) is used to actively control voltage devices, including on-load tap changer-fitted LV transformers and MV capacitors. Using three realistically modelled radially operated UK MV/LV networks serving 37,000+ customers (94% residential, 6% commercial and industrial), our results show that a trade-off does exist, but it can be reduced with increasing network control flexibility, particularly with controllable LV transformers. Furthermore, for these networks the reduction in energy consumption due to CVR can be 20 times larger than the increase seen in MV energy losses. These insights are useful for distribution companies in assessing the overall benefits of CVR with active voltage management despite small increases in MV energy losses.

Topics & Concepts

Voltage reductionTap changerVoltageTransformerEnergy consumptionCapacitorLow voltageEnergy conservationEnergy managementFlexibility (engineering)Pareto principleElectric potential energyComputer scienceEfficient energy useParticle swarm optimizationElectrical engineeringEnergy (signal processing)EngineeringOperations managementEconomicsMathematicsStatisticsMachine learningManagementOptimal Power Flow DistributionSmart Grid Energy ManagementMicrogrid Control and Optimization