# Household Energy Management
## 5 Discussion  
The EEM algorithm proposed in the article is an on-line algorithm, i.e., for each moment the value of power consumed from the power grid and RES is analyzed, and the costs for energy consumed are reduced while ensuring a certain level of consumer comfort. The algorithm takes into account the presence of appliances and smart appliances. After a slight modification, the developed algorithm can also work with other price programs, e.g., RTP. The evaluation tests which were carried out on the prosumer block show that the applied GRASP algorithm with a given optimization criterion obtains the most favorable results of setting the power levels of appliances with a small number of iterations. The novelty of the EEM algorithm is:  
* to increase consumer comfort by introducing additional \(U\) and \(U_{SEL}\) coefficients in the objective function,
* using smart appliances with a certain number of power levels to be used in its reduction process and consumer power reduction thresholds (\(P_{lmax}\)),
* the ability to reduce power charged by the household to the level of \(P_{h\_DSO}\) on demand DSO to reduce peak demand and excess power generated by RES (\(P_{RES\_DSO}\)). The tests confirmed that the proposed EEM algorithm can contribute to a significant reduction in energy costs; in the analyzed example, about 5.6%, compared to the user's independent use of the ToU pricing program only. With RES, the reduction will be much greater. For the analyzed example, it is around 13.6%. At the same time, this algorithm performs an educational function, shaping the consumer behavior profile towards the activation of appliances in the cheapest time zones in order to mitigate the peak demand phenomenon at a higher level of energy management. The introduction of inverters from RES provides even greater user comfort in the use of appliances installed in the household. Furthermore, the introduction of the EEM2 algorithm confirmed the possibilities of its application to increase the support of actions taken at the DSO and TSO level in the reduction of peak demand. EEM2 also translates into benefits for household energy consumers. In addition to the additional slight reduction in energy costs resulting from the ToU pricing program, there may be remuneration for not using equipment in the time set by DSO above the set power threshold \(P_{h\_DSO}\). This means that the proposed EEM algorithm makes it possible to plan energy consumer behavior and to plan energy demand. Appliances, which should be intelligent appliances, play an important role in the energy management process. Manufacturers of such appliances, in addition to being able to control the EEM algorithm and shift in time, were proposed in the article to introduce additional parameters related to work sections (_WS_), power reduction levels (_k_) and priorities. This will allow, among others uses, an increase in flexibility in the use of such appliances. Further work on the EEM algorithm is expected to take into account the existence of energy storage in households, including electric cars. Further work on the verification of other metaheuristic algorithms in order to compare their properties with the GRASP algorithm is also planned. Conceptualization, P.P., W.M. and K.P. ; methodology, P.P. and W.M. ; software, P.P. and R.S. ; validation, P.P., R.S. and W.M. ; formal analysis, P.P. and W.M. ; investigation, P.P., W.M. and K.P. ; writing--original draft preparation, P.P., W.M. and K.P. ; writing--review and editing P.P., R.S., W.M. and K.P. ; visualization, P.P. and W.M. ; supervision, P.P. and K.P. ; project administration, P.P. and K.P. ; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript. This work was partially funded by the European Union within the European Regional Development Fund (ERDF), in the context of the INTERREG V A BB-PL Programme.