Increasing the penetration of renewable power in district heating systems, the interdependency of power and heat networks increases. To counterbalance the fluctuations of renewable power, the energy consumption of the heat consumers should be coordinated with power networks. Coordinated operation of multi-carrier systems makes it possible to provide flexibility for both power and heat networks. In the heat network, residential heating systems consume a major part of the heat energy. Besides, due to buildings’ thermal inertia and storage capacity of household water tanks, the flexibility potentials of the residential buildings are relatively high. This chapter investigates the role of intelligent heat controllers in power and heat networks. To achieve the aim, first, an economic heat controller is suggested for residential heat pumps with water tanks. Model predictive control (MPC) approach is addressed to optimize the operation of the heat pump in response to wholesale electricity price and weather conditions. Afterward, a stochastic MPC approach is suggested to unlock the power-heat flexibility of heat pumps in three electricity markets with price uncertainty. The electricity market includes day-ahead, intraday, and balancing markets which are cleared from 24 hours ahead until near real-time. Finally, the control fundamentals are stated for residential buildings supplied by a mixing loop of district heating. The controller optimizes the operation of inflow and outflow valves of the mixing loop. The simulation results confirm that the suggested controllers reduce the household energy cost. Besides, they can provide up- (down-) power regulation in the opposite direction of system imbalance when a power shortage (excess) occurs.
|Title of host publication||Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks|
|Editors||Mohammadreza Daneshvar, Behnam Mohammadi-Ivatloo, Kazem Zare|
|Number of pages||30|
|Publication date||11 Nov 2022|
|Publication status||Published - 11 Nov 2022|
- Electricity market
- Model predictive