# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 4 Role of Energy Internet in Smart Grid
### Energy Internet as a Peak Load Optimizer  
EI can manage the load demand effectively in a smart grid scenario. During the peak load when the energy demand exceeds the generation, the reliability of the power supply is hampered. The scheme applied to mitigate this issue is managing the economics of the grid, based on the formula of high consumption-high payment, and vice versa, during peak hours. Thus, the peak load shaving is achieved by shifting the utilization time of the energy without varying the overall energy consumption. A novel real-time pricing (RTP) model for electricity usage and scheduling is proposed in the literature [16]. It works on the application of smart meters to collect data and optimize energy production. In the direct load control (DLC) method [62], for dispatch and unit commitment coordination, EMS will end up with a load curtailment in the peak load hours, causing system disturbance and customer dissatisfaction. In the literature [63], an evaluation of the economic feasibility of the electrochemical storage systems in the peak load shaving applications, along with the sizing and operating strategy of the battery energy storage system (BESS or DESD) is presented. Although this strategy improves the conventional electrical grid, it does not consider the EI framework as its mathematical model, and lacks the involvement of renewable energy sources. In the literature [23], an EI-based peak load shifting problem is addressed by trading energy among the end-users. The EI framework realizes the energy demand data through smart meters, and then sends this information to the control center, which distributes the energy to meet the user demands. The end-user can sell the energy from their own storage facility, such as EVs and DESDs, by just plugging the storage units into the grids. Applying real-time pricing will decrease the operating cost in this framework. The proposed model of optimal energy management system (EMS) in the literature [64] can be applied to any energy system characterized by multiple productions, storage systems, and distribution systems that have unique decision makers. The overall objective function of EMS here should be to minimize the sum of the net operating costs, as follows:  
\[Min\quad C_{TOT}+\ Co_{2}\ E_{Co_{2}} \tag{1}\]  
Here, \(Co_{2}\) is the parameter corresponding to the price of emission for one ton of coal, \(E_{Co_{2}}\) is the overall \(Co_{2}\) emissions, and \(C_{TOT}\) is the overall operating cost given as follows:  
\[C_{TOT}=C_{B}+C_{k}+C_{RES}+C_{grid}+B_{grid} \tag{2}\]  
In the above equation, the terms \(C_{B}\), \(C_{k}\), \(C_{grid}\), \(C_{RES}\), and \(B_{grid}\) represent the operating costs due to the boiler, microturbines, external grid charge, renewable energy sources, and financial benefits related to the electricity exchange with the grid, respectively. However, Equations (1) and (2) are based on \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter of Comparison** & **SG Communication [3]** & **EI Communication [21]** \\ \hline Characteristics & Information transmission & Energy and information transmission \\ Service objects & Multi-source heterogeneous data & Multiple distributed renewable energy \\  & & and multi-source heterogeneous data \\ Routing equipment & Smart meters & ER \\  & Grid interconnection & Grid interconnection \\ Functionalities & Data processing & Data, energy transmission, and processing \\  & Grid management & Grid management \\ Standards available & International standards such as & Not yet available \\  & IEC 61850 [61] and DNP3 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative features between the smart grid and energy Internet. the assumption that the energy usage cost of all of the end-users in a day, and the cost of generating (charging) the renewable energy are not considered in \(C_{grid}\). In the literature [23], [PERSON] extended this model with the consideration of the EI framework, and adopted the influence of RTP on the distribution of the electricity supply and demand.