## 5 Optimization Control Strategies in Bems
### Uses of Optimization Algorithms in BEMS
#### 5.2.6 Neighborhood Energy Optimization Algorithms for a Set of Commercial Buildings  
A single subject owns a number of the neighborhood's buildings in the single-owner scenario. The architecture for energy optimization is centralized in design. The neighborhood buildings are owned by various subjects in the multi-owner scenario, each of whom seeks to reduce their individual energy costs. The building owners additionally consent to provide flexibility to the entire neighborhood and run their own local generation and storage units in a coordinated manner to achieve neighborhood-level goals. The architecture for energy optimization is hierarchical. A centralized optimization for a single-owner neighborhood with a high level of transparency and a hierarchical two-level optimization for a neighborhood with multiple owners and a reduced level of transparency are two separate optimization algorithms. Additionally, in order to lower the neighborhood net load as viewed from the grid side and maintain the neighborhood pollution emissions below a predetermined threshold, the neighborhood energy optimization algorithms schedule the generation and storage equipment based on energy prices. It has been demonstrated that the use of flexible resources, such as thermal storage (related to thermal comfort levels) or electrical storage, enables one to pursue an economic goal while utilizing the flexibility of the local energy supply.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Optimization Techniques** & **Objectives** & **Contributions** & **Ref.** \\ \hline HEDE based on EDE and HSA & Cost, PAR, and user comfort optimization. & EDE performance is better than HSA in terms of cost reduction and HSA is better in terms of PAR as compared to EDE. & [158] \\ Dijkstra algorithm (DA) & Consumption cost, curtailed loads, grid imbalances, and used energy & DA reduced 51.72\% of the cost when attaching RER and 10.22\% of PAR. & [159] \\ Satin lowerbird optimization (SBO) algorithm & To optimize the scheduling of appliances within a discrete comfort window (DCW). & Reduced the electricity cost from \(\upmu\)29.14/ day to \(\upmu\)22.84/day, and reduction of PAR is 10.28\%. & [160] \\ Cuckoo search optimization (CSO) algorithm & Reduction in electricity cost, PAR, and minimum user waiting time. & CSOA is superior in terms of cost and PAR compared to CSA and GA. & [161] \\ Hybrid genetic particle swarm optimization (HGPO) algorithm & Optimization of electricity bills, carbon emissions, user comfort, PAR. & Reduction of electricity cost by 25.55\%, PAR by 36.98, and carbon emissions by 24.02\%, respectively. & [162] \\ Genetic BPSO (GBPSO) algorithm & Electricity bills and PAR optimization. & GBPSO is better in terms of both cost reduction and curtailment of PAR compared to GA, BPSO, WDO, and BFOA. & [163] \\ Binary backtracking search (BBS) algorithm & Reducing and scheduling energy usage. & BBSA provides better performance compared to BPSO, in terms of reducing energy consumption, total electricity bills, and saving the energy of certain loads at peak hours. & [164] \\ \hline Harmony search gray wolf optimization (HSGWO) algorithm. & Efficient scheduling & HSGWO performs better than HSA and GWO in terms of cost and user comfort. & [165] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of others optimization approaches in BEMS.