## 4 Conclusions  
The aim of the study was to verify in a simulation environment an algorithm for limiting power peaks in an electrical grid in a multiple consumer two-position controlled system. The study was carried out on a model of an existing Creep Test Laboratory, where power peaks are caused by unsynchronised switching of 389 electric heaters. The presented algorithm was tested in a multi-load system characterised by high thermal inertia, including two types of energy consumers: furnaces of three-zone single-sample creep test machines and two-chamber furnaces of multi-sample machines, whose dominant time constant was about ten times greater. The parameters of the algorithm were adjusted to a smaller time constant, assuming that the delays caused by the algorithm would not affect the loads with a large time constant. This solution simplifies the master control system without using the much higher energy storage potential of high inertia loads, and it was possible to achieve a sufficient level of power peak suppression. As the algorithm does not distinguish between types of energy storage, it can be applied to grids with connected loads characterised by high thermal, electrical or mechanical inertia. The only condition is to ensure that the parameters of the algorithm are adjusted so that the switch-on delays for the loads with the shortest time constant do not cause the permissible deviations of the process variable to be exceeded. Another issue is the limitation of power peaks in systems containing both loads with high inertia for the process variable and those with no or minimal energy storage capacity. In this case, the reduction of power peaks will be limited to energy storage loads only. This disadvantage for grids with mixed load types can be eliminated by developing and adding an additional module that, based on knowledge of the switching on of non-inertial devices, e.g., using predictive algorithms, will compensate the power peaks using the energy storage created from loads with high inertia. The research demonstrated the effectiveness of the developed algorithm in systems where the controlled objects are characterised by relatively large time constants compared to the sampling period of the binary control signal. The authors also conducted research on reducing power peaks using task scheduling techniques. The problem of limiting power peaks was reduced to a task scheduling problem with a variable number of machines, and then, using heuristics, it was simplified. In this case, the obtained standard deviations were of the order of 0.5 kW, so similar or even slightly better than those presented in this article, but temperature stabilisation of the furnaces was not achieved. The slightly better suppression of power peaks was occupied by temperature changes in the furnaces of up to several degrees Celsius. In addition, the computational complexity of the task scheduling algorithm was significantly higher. The algorithm presented in this paper contained 20 lines of code in MATLAB. In comparison, the code of the task scheduling algorithm, after introducing the simplification, contained almost 400 lines of code. Thus, it can be concluded that for a system where the power does not change rapidly, the proposed algorithm based on power averaging represents a compromise between suppressing power peaks and not affecting the process variables of the connected loads. Because of its low computational complexity, it can be implemented in large industrial installations as well as installations using programmable controllers with low processor power. The simplicity of the algorithm is due to its specific application in systems where the average power generated by the load changes slowly, as is the case in this particular installation. Obviously, this limits the application of the solution to a specific group of systems, where control is reduced to the stabilisation of a process variable.