The Suomenoja power plant supplies the Espoo and Kirkkonummi district heating network in Finland
The Suomenoja power plant supplies the Espoo and Kirkkonummi district heating network in Finland

Control solutions for district heating networks can ensure network stability and balance heat production and consumption. Jyri Kaivosoja, Markku Muilu and Juha-Pekka Jalkanen present Metso’s total control solution for district heating networks.

Heat production in a large district heating network is typically supplied by multiple distributed cogeneration plants and heating stations. The daily variation in total heat consumption is often more than the control range of any single heat producer. Together with rapidly changing electricity and fuel prices, this creates challenges to controlling the heat producers economically while maintaining the stability of the district heating network. An economically optimised and dynamically co-ordinated control solution is needed. Here we look at Metso’s control solution for a large district heating network with total heat power of about 1300 MW.

The control solution ensures the stability of the network and balances heat production and consumption by measuring and controlling over 30 pressure differences along the network. The production rates of all cogeneration plants, heating stations and pumping stations are controlled by the solution. The control solution also includes optimisation of the supply temperatures of all heat producers.

The core of the solution is a multivariable predictive controller (MPC). The control solution improves the economic efficiency and stability of the network and reduces pumping costs.

Targets of the optimisation

Espoo and Kirkkonummi district heating network is one of the biggest networks in Finland. It consists of over 800 km of piping and the volume is over 60,000 m3. Heat is supplied to the network by the Suomenoja power plant, with four main units, and by 10 heating plants distributed in various places in the network. There are also 12 pumping stations.

The main controls of the district heating network are: control of the average pressure; control of the supply temperature; and control of the pressure differences.

The end customer requires enough pressure difference over their local heat exchangers and enough high water temperature before the heat exchanger.

The main problems have been to control the supply temperatures of the production units and optimise pressure differences in the network. For instance, the supply temperature from the Suomenoja power plant could vary from 15°C-20°C during a day when the plant was running at full power. The main reason is that, when running at full and constant power, there was no capacity to change power – and now, when the pressure difference was kept at a constant level, the flow through the district heat exchangers was changed according to changes in consumption. When the district heating power output was constant and the flow changed, the supply temperature changed uncontrollably.

The fast variation of the supply temperature is also a problem, because it stresses the network and increases maintenance costs. Especially in some unplanned shutdowns, the supply temperature from one production unit could drop quickly, causing stress and leakage in the piping. In some cases in Suomenoja, an unplanned shutdown in one unit has caused another unit to shut down because of the sudden increase in the other unit’s district heating flow.

There are over 30 pressure difference measurements in the network and it has been difficult to manually keep all of them in the optimal area.

The supply temperature target is to follow up its nominal curve, which is a function of outdoor temperature. If the supply temperature is too high, the network’s heat losses increase, electricity production decreases and pumping power consumption decreases. The first two effects are negative and the third is positive. The most economical way is to use supply temperatures that are as low as possible, but at the same time take care that all consumers have enough high temperature and enough pressure difference.

The targets for the optimisation of controls are:

  • Keep the supply temperature of all production units as near the target temperature as practically possible;
  • Avoid fast and large variations in the supply temperature, especially in shutdowns and starts;
  • Keep the pressure differences in the network inside allowed fluctuations around target values;
  • Pressure differences in longer periods should be controlled mainly with the power outputs of district heating production units;
  • The power output of the least economical production unit should control the pressure difference, with the most economical units kept in full power;
  • Minimise the auxiliary power of the pumping stations.

Network structure

The network layout includes multiple large circular loops, but also long branches. The locations of the heating stations are not perfectly balanced in relation to the heat consumption, having excess heat capacity in the east and shortages in the northwest. The Suomenoja power plant is located at the star point of the network, on the southern coast. Pumping stations are needed to distribute the heat to the customers.

Supply water temperature controls

The setpoint of the supply temperature is interpolated from the nominal temperature curve using the weighted average of 10 outdoor temperature measurements. The network operator can also adjust the final supply water temperature setpoint by setting its bias to the nominal temperature. This bias is used in order to accumulate or discharge heat energy into the network when the outdoor temperature is predicted to decrease or increase, respectively.

All heat-producing units use the same temperature setpoint to minimise thermal stress in the pipelines at the borders of different supply areas. Figure 1 presents the formation of the supply water temperature setpoint.

Figure 1. The setpoint of the supply temperature is interpolated from the nominal temperature curve
Figure 1. The setpoint of the supply temperature is interpolated from the nominal temperature curve by using the weighted average of 10 outdoor temperature measurements. The operator can adjust the final setpoint

The supply water temperature is controlled in each unit by manipulating the water pumping with temperature-flow-cascade control. With this control strategy the supply water pumps keep the water temperature at the desired setpoint and respond to changes in the unit’s heat power by controlling the supply water flow correspondingly.

Figures 2a and 2b compare the supply water temperature controls before and after commissioning of the District Heating Manager.

Figure 2a. The supply water temperature of the Suomenoja power plant was fluctuating with the old controls
Figure 2a. The supply water temperature of the Suomenoja power plant was fluctuating with the old controls
Figure 2b. With the District Heating Manager the supply water temperature is stable
Figure 2b. With the District Heating Manager the supply water temperature is stable

Pressure difference controls

The pressure differences between the supply and return lines are measured at 32 different network locations with online measurements. These measurements are aggregated into eight areal groups that can be controlled independently. An overall weighted average of all 32 measurements is also calculated and controlled.

The overall average pressure difference in the entire network is controlled by manipulating the total heat power of the heat-producing units. This requires that the supply water pumps respond to changes in heat power.

The eight aggregated pressure difference groups are also controlled by manipulating the heat power of the heat-producing units, but also by the booster pumping stations. This control distributes the overall pressure difference optimally across the whole network. This way the pressure difference is adequate for all customers, and the overall pumping costs are minimised.

Figure 3 presents the pressure difference measurements, along with their setpoints, aggregation into areal groups, and calculation of their overall weighted average.

Figure 3. The pressure differences between the supply and return lines are measured at 32 different locations in the network with online measurements
Figure 3. The pressure differences between the supply and return lines are measured at 32 different locations in the network with online measurements. These measurements are aggregated into eight areal groups that can be controlled independently. The overall weighted average of all measurements is also calculated and controlled

The key benefit of the MPC, in comparison to single-input single-output controllers, is that it simultaneously takes into account the dynamic interconnections of all manipulated variables with all controlled variables. Also, the constraints of manipulated and controlled variables can be handled and taken into account elegantly. Metso has strong experience in utilising MPC in CHP plants in the forest industry, i.e., Steam Network Manager controls.

Manipulated variables are: heat power of the four main units at the Suomenoja power plant; heat power of the six main heating stations; and rotation speeds of the booster pumps at the 10 main pumping stations.

The MPC optimises the control performance by utilising the capacities of all units. It also improves stability in disturbance situations. Figure 4 presents how the tripping of one heating station is compensated for with a pumping station.

Figure 4. Tripping of the Kirkkonummi heating station is compensated for with the Sarfvik pumping station
Figure 4. Tripping of the Kirkkonummi heating station is compensated for with the Sarfvik pumping station. The pressure difference at the Kirkkonummi area is reduced for just a few minutes. Since the missing heat production of the Kirkkonummi heating station is compensated for with other heating stations, the average pressure difference remains at a normal level

Heat production optimisation

In order to optimise the economics of heat production, the loads of the most economically beneficial units should be maximised and less efficient units minimised. This is done by setting the units of the Suomenoja power plant and the heating stations into a specific priority order. The priority order can be changed according to fuel and electricity prices.

Figure 5. The units of the Suomenoja power plant and heating stations are run according to the selected priority order
Figure 5. The units of the Suomenoja power plant and heating stations are run according to the selected priority order. The priority order can be changed according to fuel and electricity prices

Conclusions and perspectives

There are multiple targets in the optimisation of the control of district heating networks. Large variations in heat consumption and rapidly changing electricity and fuel prices create challenges in controlling the network. Heat production needs to be economically optimised while maintaining the stability of the district heating network.

These targets are met with the District Heating Manager control solution. Supply water temperatures are stabilised by temperature-flow cascade controls using supply water pumping. The pressure differences at multiple different locations in the network are controlled by the MPC. Heat production from multiple cogeneration plants and heating stations is co-ordinated and optimised according to economic priority. The stability of the network is improved in normal operation, but also in disturbance situations.

The District Heating Manager control solution also makes network operation straightforward, since all of the heat production units are controlled with a single control strategy. Furthermore, the graphical user interface helps the operators to see the overall situation in the network easily.

Jyri Kaivosoja is a Specialist in Performance Services, and Juha-Pekka Jalkanen is Director of Plant Performance Solutions, at Metso Automation. Markku Muilu is Technical Manager at Fortum Power and Heat.

For more information please visit www.metso.com

This article is based on a highly recommended paper at the POWER-GEN Europe Best Paper Awards