# Future Renewable Energy Communities Based Flexible Power Systems
## 2 Management of Energy Resources
In addition to contributing to an electric network in a smart city, in order to make the energy source and supply more stable, uninterrupted, and reliable, an interactive storage structure can reduce energy costs [21]. An energy management system allows the prosumer to have the flexibility to use its own power during peak periods when energy rates are high, or to sell it to the grid whenever is most advantageous. Another example is the use of the BSS to supply energy temporarily to neighbors during high consumption of energy. This decision would be taken by a central controller to avoid electric energy travelling long distances, since the residence is usually far from the main power plants. Consequently, there is a reduction in distributed power losses. Therefore, it is important to have an integrated energy system capable of reducing energy costs by ensuring an efficient process and easy access to reliable energy. The main objectives of an energy management system is to minimize the electricity bill, to guarantee a reliable energy supply, to distribute the demand, and to reduce the number of storage means of charging/discharging cycles. As an example, let us assume that in a particular case, it has to be taken decisions about the need for charging and discharging in storage batteries that has to be made every three hours based on averaged measurements for one hour. Minimizing the electrical bill would be achieved by maximizing the power coming from the source or the one stored in a battery set:  
\[P_{grid}=P_{load}-P_{pv}-P_{bat}-P_{losses} \tag{1}\]  
where  
\(P_{grid}:\) grid power  
\(P_{load}:\) load power  
\(P_{pv}:\) PV power  
\(P_{bat}:\) battery power  
\(P_{bat}>0\) means battery charging  
\(P_{bat}\leq 0\) means battery discharging  
\(P_{losses}:\) power losses  
In addition, the state of charge is defined as:  
\[SOC=\frac{Q}{Q(1-\int_{t}^{t+T_{0}}i(t)dt)} \tag{2}\]  
where \(Q\) is the capacity of the battery and \(i(t)\) denotes the charging/discharging current According to (2), if the battery is fully charged at \(t=0\), and there is no charging/discharging current injected/consumed to/from the battery (i.e., \(i(t)=0\)), then SOC = 1. When the battery is discharging, then \(i(t)<0\), which will in turn reduce the SOC based on the discharging current. In contrast, if the battery is charging, then \(i(t)>0\), which will increase the battery SOC. Let us suppose a small village with data and characteristics due to 24 h of an electrical demand for 30 residences, 15 shops, and 5 factories. Thus, there are 8760 h of solar energy data, where 8760 is the number of hours in one year. Assume that the daily load demand for this district repeats itself every day, during 365 days (one whole year). In a real project, it is necessary data for 8760 h of load, but this is a simplified case study. If the system could capture all the solar energy during the day and use fuel cell during the night, a simple energy balance equation would be [22]:  
\[ \begin{array}{l}\int\limits_{0}^{8760}(solar\ radiation*PV\ total\ conversion\ efficiency)dt\\ +\ \int\limits_{0}^{8760}(wind\ energy*turbine\ system\ total\ efficiency)dt\\ =\int\limits_{0}^{8760}(loads+\ battery+losses+shunt\_load)dt\end{array} \]