## 7 Optimization Techniques for Demand-Side Resources in Active Distribution Networks
### Classical/Deterministic Optimization Methods  
Classical or deterministic optimization methods are a cornerstone of problem-solving across various domains, from engineering and economics to logistics and operations research. These techniques provide systematic and structured approaches to finding the best possible solution for a given problem within a set of constraints. Classical or deterministic optimization methods play a pivotal role in optimizing demand-side resources (DSRs) within active distribution networks. These methods are indispensable for effectively integrating and managing DSRs to enhance grid reliability, efficiency, and sustainability. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Type** & **Implementation** & **Resources** & **Limitations** \\ \hline
[131] & MILP (Classical) & Reduced annualized costly by optimally selecting several system components and renewables on a smart grid. & Grid-field with microgrid with solar PV, CHP, backed-up boliers, and loads (Simulation-based). & Strugges to handle the optimization of multi-input and output systems. \\ \hline  & Multi-objective framework using MILP (Classical) & Avoid power export by optimizing the multiple ospearning systems, such as combined heat and power in microgrid residential areas. An operational planning method to mimic energy loss characteristics between storage tanks and a hot water cleaning model regarding energy loss on network pipes were developed using MILP. This resulted in a reduction in residential units involved in the hot water supply network. & Grid-field microgrid with renewable energy sources, battery storage, and loads (Simulation-based). & Slow in transient and systems with a high volume of dynamics. \\ \hline
[133] & Fuzzy logic-based decision-making framework (Heuristic) & Optimize power dispatted to the grid through storage systems. Maximize electricity generation through renewables and revenue to Smartworth (Simulation-based). & Grid-field with microgrid with renewable energy sources, battery storage, and loads (Simulation-based). & Slow in transient and systems with a high volume of dynamics. \\ \hline
[134] & MILP(Classical) & Investigations how the combination of electrical and thermal storage can reduce energy cost by enabling the microgrid to improve using its power produced in-house. & Grid-field with microgrid solar PV, geothermal heat pumps, solar thermal energy storage, battery storage, and loads (Simulation-based) (Real data). & However, high investment costs made them unprofitable at the current price condition. \\ \hline
[135] & Improved teaching learning-based optimization (Heuristic) & Minimize the impact of intermittency and fluctuation of renewables by controlling DG output power, altering network topologies, and managing demand-side load. & Grid-field microgrid with wind turbines, solar PV, and loads (Simulation-based). & Inability to predict the future behavior of the system. \\ \hline  & Advanced model prediction control (Heuristic) & Maximize the high penetration of renewables in the microgrid and minimize the running cost by solving optimization problems at each sampling time while meeting the demand and accounting for technical and physical constraints. & Grid-field microgrid with battery storage, fuel cells, wind turbines, hydrogen electrolyzer, solar PV, hydrogen tanks, and loads (Simulation-based). & Scalability, complexity, and controllability challenge. \\ \hline
[137] & MPC (Heuristic) & Minimize energy cost and maximize battery lilegpan by employing a microgrid central controller to optimally choose the adequate pattern for charging and discharging. & Grid-field microgrid with energy storage, wind, and solar PV (Simulation-based). & Slow in handling fast transient systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimization methods and limitations.