Climate change is a significant and ever-growing threat to societies around the world. To reduce its consequences, immediate changes to reduce anthropogenic greenhouse gas emissions (GHGs) are needed. A major contribution to global GHGs is from fossil fuel thermal generation supplying a majority of the world’s electricity. Its extensive use is due to a variety of reasons including its controllability, enabling it to fulfil a range of roles to maintain security of supply for electricity systems (e.g. peaking, baseload and load following). To transition to a low-to-zero emission system, thermal generation needs to be replaced with renewable generation such as wind and solar. Wind and solar generation production is intermittent and has limited controllability. As a result, it can only reliably provide energy (not capacity) and cannot fulfil the same roles as thermal generation, hence other assets will be required to maintain security of supply.

Systems with established hydro generation and reservoir storage have an advantage when integrating renewable generation. In conjunction with hydro reservoir storage, wind and solar generation intermittency is largely mitigated as water can be conserved in reservoirs during high renewable production periods, effectively storing this energy. Hydro generation itself has no operational GHG emissions and is more controllable than thermal generation, hence it can take the security of supply roles.

However, hydro generation comes with its own risks. Low inflow years (droughts) can threaten security of supply, which in New Zealand is often managed with thermal generation. Also, managing a hydro electric system is complex, in part due to reservoirs linking the current generation dispatch decision to all future dispatch decisions. As such, specialist hydro scheduling optimisation methodologies have been developed to assist with hydro system management.

To study the renewable generation transition of hydro-thermal electricity systems, a hydro scheduling modelling tool is developed for system studies. It consists of two sub- tools: a Price Discovery and System Operation Simulation (SOS). The Price Discovery uses a deterministic Dynamic Programming based approach to produce optimal water value functions, one for each reservoir in the system model. The SOS simulates the operation of the system using these water value functions to determine the hydro generation prices. Time series it produces include storage trajectories, generation dispatch and transmission power flow. A deterministic Price Discovery is deem sufficient for the system studies particularly for New Zealand, although in future work, it can be extended to consider the stochastic nature of inflows.

To accompany the hydro scheduling modelling tool, a New Zealand system model is developed. It is a two-transmission-node, two-reservoir model representing New Zealand’s North and South Islands. Although simple, the model sufficiently represents New Zealand system’s major constraints and dynamics. These include the transmission constraint between the North and South Island, the geographical mismatch between demand (majority in the North Island) and hydro resource (generation, storage and inflows, majority in the South Island) and the temporal mismatch between hydro inflows (high in spring and summer) and demand (high during winter). These issues are evident in New Zealand’s system given its limited hydro storage, being only 9% of annual demand.

A unique aspect of the hydro scheduling modelling tool is that it uses a high temporal resolution (half hourly to daily) over a medium term time horizon (one year). This allows an investigation into the impact of generation and transmission capacity constraints on the water value functions and system operation. A high temporal resolution is valuable given wind and solar generation’s intermittency.

To demonstrate the modelling tool’s capability for quantifying the impacts of the renewable generation transition, it is applied to a 2030 scenario variant of the New Zealand system model. This model has increased demand and two of the thermal generators are considered decommissioned. The study determines the amount of additional renewable generation require to avoid empty reservoirs and consequential demand curtailment and the value of additional hydro storage and generation capacity and transmission capacity. The value of diversifying between additional wind and solar generation is also investigated.