From an environment perspective, the increased penetration of wind and solar generation in power systems is remarkable. However, as the intermittent renewable generation briskly grows, electrical grids are experiencing significant discrepancies between supply and demand as a result of limited system flexibility. This paper investigates the optimal sizing and control of the hydrogen energy storage system for increased utilization of renewable generation. Using a Finnish case study, a mathematical model is presented to investigate the optimal storage capacity in a renewable power system. In addition, the impact of demand response for domestic storage space heating in terms of the optimal sizing of energy storage is discussed. Finally, sensitivity analyses are conducted to observe the impact of a small share of controllable baseload production as well as the oversizing of renewable generation in terms of required hydrogen storage size.

The domestic heating, ventilation, and air-conditioning load promises a good prospect for electrical aggregators to consider it for demand response. This article presents a user-centric demand response control for scheduling the electric space heating load under a price and load uncertainty environment. The objective of the framework is to minimize a weighted sum of the expected payment, loss of comfort, and financial risk of a customer while strictly considering the end-user preferences. The household thermal behavior is modeled via an accurate two-capacity building model. The price and load uncertainty is modeled using a scenario-based stochastic programming approach. The proposed decision model is formulated as a non-linear programming problem that can be simply solved via commercially available solvers. The effectiveness of the formulation is demonstrated by applying it to a typical customer. The simulation results demonstrate that the decision mechanism allows consumers to compromise among electricity payment, thermal comfort, and risk exposure based on their thermal comfort preferences and risk priorities.

The management of transformers' capacity is a vital task for planners and asset managers due to their high cost. This paper proposes an optimization model for capacity management of transformers in a substation over long-run. The model considers the present worth costs of investment, losses, maintenance, reliability, and the salvage value of transformers for providing the optimal selection and scheduling of multistage transformer installations and their refurbishments. In the optimization model, growing failure rate of transformers along with their age is incorporated and the cumulative loss-of-life (LOL) of transformers is also used in determining their salvage value. The developed model is applied for planning and management of transformer capacities for a residential load dominant two-transformer primary distribution substation over a period of 40 years. The simulations are performed for various case studies representing the situations encountered by utilities. An extensive sensitivity analysis is also conducted for several conditions of the system parameters. The numerical results indicate the worth of inclusion of variable failure rate and LOL of transformers in their capacity management.

In power systems today, considerable developments are being made in the energy transition from centralized fossil fuels to renewable distributed generation (DG) sources. However, this ongoing progress has presented several challenges to the operation and planning of distribution systems due to the variability of intermittent renewable generation. Demand response (DR) is widely regarded as a feasible tool to provide a seamless transition by altering the load profiles according to the intermittent generation profile. However, the resultant volatile power flows can be taxing for network capacity. This paper offers a framework from the distribution system operator's perspective for the optimal utilization of DR between DG curtailment mitigation and network management. The application of the developed framework to a generic Finnish distribution system demonstrates that the benefits of DR should be envisioned for network management as long as the wind curtailment rate is below a certain level. This means that, beyond the threshold energy curtailment rate, the distribution system operator would be more economically efficient by making network reinforcements.