## 11 Conclusions  
In this review paper, existing research on DSM operations, including the various DGs and an area that has witnessed significant interest in the energy management domain in the last few years, was reviewed extensively. The general structure, operation, and optimization models of DSM and DG-DSM integration into the present smart grid scenario were discussed and represented. New concepts such as waste-to-energy were explored through a brief study, as were their implementations in test case scenarios. The optimization aspect of DG-DSM scheduling was tabulated and represented, with emphasis placed on the objective function formulation, constraints or limitations, and the selection and parameterization of decision variables. With the expectation of an increase in the adoption of various types of DG, it is estimated that DSM operations can play a valuable opportunity for the customers and utility aggregators to be active participants in the scheduling, dispatch, and market-oriented trading of energy. The research directions that this review article provides can help researchers identify potential gaps, which have been discussed previously, and they can be given due importance in finding solutions to the existing issues and challenges.  
S.P.: Conceptualization, Writing--Review & Editing, Writing--Original Draft. S.M.: Writing--Review & Editing. P.K.R.: Investigation, Formal Analysis, Supervision. B.K.S.: Writing, Review & Editing, Supervision. S.M.P.: Review & Editing. H.K.: Writing, Review & Editing. A.F.: Supervision. M.T.-V.: Supervision. B.A.S.: Supervision, Review & Editing. M.S.: Supervision, Review & Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
\begin{tabular}{l l} HRL & Hierarchical Reinforcement Learning \\ ADMM & Alternating Direction Method of Multipliers \\ KKT & Karush-Kuhn-Tucker \\ PC & Peak Clipping \\ VF & Valley Filling \\ LG & Load Growth \\ \(P_{grid}\)_(_h_) & Transfer of Power from the Grid to Load (kW) \\ \(D_{e}\)_(_h_) & Electrical Energy Demand at Hour \(h\) (kWh) \\ \(SoC_{min}\)_(_h_) & Minimum SoC at Hour \(h\) \\ \(E^{h}\)_{hutt_ & The Battery Energy at Hour \(h\) \\ \(d_{r}\) & Load Duration \\ \(P_{max}^{max}\)_{grid}(_h_) & The Maximum Power Draw by Load from the Grid at Hour \(h\) \\ \(B_{gj}\)(_l_) & The Energy of jth Storage Device \\ \(P_{gsj}\) & Power Emission from jth Storage \\ \(C_{rt}\)_g_ & Cost of Renewable Energy Production \\ \(P_{b}\) & Penalty of Battery \\ \(P_{H}\) & Penalty of Hydrogen \\ \(P_{HT}\) & Penalty Hydride Tank \\ \(CF^{kLB}\) & Cost of Reliability Operations \\ DSM & Demand-Side Management \\ EV & Electric Vehicle \\ SG & Smart Grid \\ EE & Energy Efficiency \\ SoC & State of Charge \\ SoH & State of Health \\ RTP & Real-time Pricing \\ DoD & Depth of Discharge \\ ISO & Independent System Operator \\ ADR & Automated Demand Response \\ UC & Unit Commitment \\ ANN & Artificial Neural Network \\ LP & Linear Programming \\ ACO & Ant Colony Optimization \\ DE & Differential Evolution \\ EMS & Energy Management System \\ IPGA & Improved Parthenogenetic Algorithm \\ MPC & Model Predictive Control \\ RMILP & Robust Mixed-Integer Linear Programming \\ CVaR & Conditional Value at Risk \\ MPSOPF & Multi-Period Security Constraint Optimal Power Flow \\ DL & Deep Learning \\ RL & Reinforcement Learning \\ PA & Pursuit Algorithm \\ MRS2R & Multi-EV Reference and Single-EV Real-time Response \\ MILP & Mixed Integer Linear Programming \\ TSO & Transmission System Operator \\ LS & Load Shifting \\ LS & Flexible Load Shifting \\ LR & Load Reduction \\ \(P_{hutt}\)_(_h_) & The Net Output Power of the Battery in (kW) \\ \(SoC_{max}\)_(_h_) & Maximum SoC at Hour \(h\) \\ \(SoC\)_(_h_) & SoC at Hour \(h\) \\ \(P_{cl}\)_(_h_) & Power for Charging at Hour \(h\) (kW) \\ \(P_{max}\)_(_h_) & Maximum Power at Hour \(h\) (kW) \\ \(B_{gj}\)(_t_) & Energy Bids of ith DG \\ \(P_{gi}\) & Power Generations of ith DG \\ \end{tabular}