It has been proven that expanding transmission capacity facilitates the achievement of net-zero targets by improving variable renewable energy (VRE) utilization (IEA, 2023). However, transmission expansion plans have traditionally only been assessed on the metrics of operational cost savings and curtailment reduction; this completely disregards the reliability benefits of expanded transmission and the quantification of increased access to high-value renewable potential (ESIG, 2022). A multi-value benefit planning framework has been developed by ESIG (2022) to more holistically assess the value of transmission expansion. In the framework, the metrics used in assessing transmission benefits still include operational cost savings, but also now includes five other metrics: emission reduction, renewable expansion capital cost savings, risk mitigation under uncertain future conditions, resource adequacy analysis, and resilience benefits. Using a mixed-integer optimization model to assess transmission expansion and a linear optimization model to evaluate grid operations, we have applied this multi-value planning framework to transmission corridors that show significant opportunity for expansion under the Canadian Energy Regulations (CER): British Columbia and Alberta, and Saskatchewan and Manitoba. Results indicate that there are significant benefits of expanding transmission in terms of improving the resilience and resource adequacy of the electricity grid, which would have gone unquantified with traditional transmission expansion assessments. This multi-benefit analysis can be used to quantify the value of interprovincial transmission and analyse the specific benefits gained through transmission expansion more fully.

References:
ESIG. (2022). Multi-Value Transmission Planning for a Clean Energy Future: A Report of the Transmission Benefits Valuation Task Force. Energy Systems Integration Group. https://www.esig.energy/ multi-value-transmission-planning-report
IEA. (2023). Net Zero Roadmap: A Global Pathway to Keep the 1.5 °C Goal in Reach. IEA. https://www.iea.org/reports/net-zero-roadmap-a-global-pathway-to-keep-the-15-0c-goal-in-reach

When operating large power systems that include controled (hydro) and uncontroled (wind) production, optimizing the use of the reservoirs is the central task. One of the main difficulties is handling the uncertainties associated with water and wind inflows, particularly in a northern region like Quebec where water storage is managed over months, even years.

We model a large part of the region's hydroelectric capacity, including five main reservoirs and their interdependence. The objective is to satisfy load while maximizing profit, with sales in the event of overproduction and purchases in the event of power shortages.

We apply a reinforcement learning (RL) approach, with a simple REINFORCE policy gradient method. Benchmark methods such as Stochastic Dynamic Programming (SDP) or Stochastic Linear Programming (SLP) are limited, as the computational effort increases exponentially with the number of reservoirs (SDP) or with the representativity of the inflow profiles (SLP).

Our research focuses on scheduling electricity generation of hydro and wind power utilizing a multi-stage stochastic optimization approach since the variability of inflows and wind is a major concern. Using Quebec's hydro and wind power networks as a case study, we investigate long-term reservoir management strategies to meet the demand while minimizing the total costs. We applied a state-of-the-art technique Stochastic Dual Dynamic Programming (SDDP) to find the optimal generation policy. We also compare the results with a deterministic policy that utilizes the expected values to find the optimal policy. The last part of our analysis is a numerical investigation of expanded generation capacity. In particular, we examine the trade-offs between the costs of new capacity construction and the reduction of load shedding and power purchasing costs.