Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts
This addresses the challenge for distribution network operators in managing active networks with distributed energy resources, though it appears incremental as it builds on existing forecasting and optimization methods.
The paper tackled the problem of suboptimal solar export limits in low-voltage networks by designing a conditional generative adversarial network (CGAN)-based model to forecast household solar generation and electricity demand, which was used in chance-constrained optimal power flow to compute fair operating envelopes under uncertainty, resulting in improved outcomes.
The increasing penetration of distributed energy resources in low-voltage networks is turning end-users from consumers to prosumers. However, the incomplete smart meter rollout and paucity of smart meter data due to the regulatory separation between retail and network service provision make active distribution network management difficult. Furthermore, distribution network operators oftentimes do not have access to real-time smart meter data, which creates an additional challenge. For the lack of better solutions, they use blanket rooftop solar export limits, leading to suboptimal outcomes. To address this, we designed a conditional generative adversarial network (CGAN)-based model to forecast household solar generation and electricity demand, which serves as an input to chance-constrained optimal power flow used to compute fair operating envelopes under uncertainty.