## 5 Conclusions  
The proposed rule based Energy Hub optimisation algorithm reduces the complexity of computation, improves system dynamic performance (the time step in this paper is one minute compared to previous work which an hour or half hour) and further reduces system operational cost by 2.92% a day compared with previous work, representing a 14.5% improvement on previous work. The optimisation approach is more real world feasible, because in this paper, it is assumed that CHP has binary states of operation. This assumption reduces the time delay between set points and actual output arising from dynamic operation of CHP, therefore reducing inaccuracy in the optimisation when deployed in the real world. Meanwhile, it maximises the CHP efficiency by reducing energy loss since when in operation the plant operates for longer at full capacity. Finally, the modified CHP control algorithm reduces the cycling stress on the CHP extending the plant lifetime. For the first time in the context of domestic CHP, dual battery storage systems are deployed together to store surplus electricity thus extending the life time of each battery.  
The daily energy cost from the combined hub control algorithm is 464 pence, which gives about \(\epsilon\)1.38 energy saving a day. In the case study, the ratio between the control algorithm's savings and the theoretical minimum savings are over 96.8%, and are shown to be always over 88% for a whole year's data, using conservative theoretical minimum assumptions, showing that this is a very powerful algorithm to optimise energy consumption in a domestic building.  
To further increase daily saving, there are four suggested approaches. The first one is to increase cost from the combined hub control algorithm to theoretical minimum ratio. To increase this rate, more precise 'Gap' and 'Glitch' diagnostic system needs to be built. However, this will significantly extend computation time and system complexity. The second way is to reduce theoretical minimum cost itself which can be achieved by improving storage system efficiencies and improving CHP generation efficiencies. This will happen as individual technologies improve. Thirdly, careful sizing of the CHP capacity is important because too small a plant will lead to an energy shortage that must be supplied by the grid, whereas too large will not justify the benefit of CHP turn-on to meet load. By optimising the CHP capacity both the efficacy of the combined hub control algorithm in reaching the theoretical minimum can be increased, and at the same time, the theoretical minimum itself can be reduced. This is the most promising way to reduce optimal operational cost. Finally, as suggested by [43], domestic users may benefit from building interconnection with many local energy systems through importing energy from different local energy systems based on the price factor.  
Supported by special fund project of Beijing key laboratory (Z171100002217071) and innovation fund project of China Electric Power Research Institute (YD83-17-002). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] analyzed the data; [PERSON] revised the paper; [PERSON] wrote the paper.  
The authors declare not conflict of interest.