*7.5. DSM with EV and ESS*

The automobile sector is presently witnessing a surge in sales of EVs with the transition to battery-based power delivery from conventional gasoline and diesel as sources of transportation fuel being the prime focus. This has seen a rise in the adoption of more efficient and high power density battery systems to be implemented at the EV end-user side. Higher capacity batteries can be configured from a backup or buffer storage system standpoint. A bidirectional implementation of these battery-enabled mobile EVs can allow for dispatch strategies to be collectively aggregated and disbursed by DNOs as a virtual power plant system through DSM strategies. Similarly, DSM can allow intelligent control of EVs, charging and discharging according to adaptive schedules, which will benefit the power system. Nonetheless, issues pertaining to EV adoption prevent the large-scale integration of DSM in EV-based programs due to:


These issues and challenges must be addressed to make EMS architecture more robust and reliable. After resolving these challenges, the optimization techniques can be integrated seamlessly into the EMS architecture.

### **8. Optimization Methods**

The earlier literature presents various ideas about different types of mathematical approaches to solve the DSM problem. Techniques such as linear programming, dynamic programming, non-linear programming, game theory approach, and particle swarm optimization set a mark for solving the DSM objectives. Recently, hybrid techniques, such as gray wolf optimization (GWO), harmony search (HS) algorithm, enhanced differential evolution (EDE), etc., have drawn the interest of researchers in this field. Many of these optimization approaches and real-world implementations of DSM (mostly on residential premises) were discussed in [26], with distinct classifications between each approach and classification. The various objectives and constraints are discussed in Table 3, and different types of optimization techniques are broadly listed in Table 4, below.


**Table 3.** Classical technique-based single objective optimization.




### **Table 3.** *Cont.*

**Table 4.** The optimization papers surveyed across DGs DSM optimization problems.






#### **Refs. Optimization Algorithm DR Programs Used Objective Function Constraints Decision Variables** [113] LP • RTP • Minimization of cost of the system • EV charging limits • EV SoC limits • Grid power consumption • Appliance schedule • Hourly tariff [114] • PS • Minimization of operational costs and emissions • Thermal unit limits • Power flow and grid constraints • PEV constraints • Power balance limits • EV SoC • Thermal generation requirement [115] • ToU • Minimization of the total cost for the consumer • Power balance limits • EV SoC limits • Power transaction limits • EV charging/discharging time • The usable capacity of EV ESS [116] • ToU • Minimization of the total cost for the consumer • EV charging limits • EV operation time limits • EV battery capacity limits • Real-time tariff [117] • RTP • Minimization of the total energy cost of a smart home • Power balance limits • Power trading limits • EV SoC limits • PV generation limits • PV generated power • EV availability [118] • PS • LS • Minimization of individual consumer costs at lower participation levels • EV SoC limits • ESS storage limits • DER generation limits • Price indicators • Customer fairness index [119] • ToU • Maximization of EVCS operating profits • EV SoC limits • ESS charge/discharge power limits • Efficiency limits • Short-term forecasted loads • Load reduction signal [120] • ToU • PS • Minimization of energy cost • EV charging limits • Cost function • Total charging demand [121] • RTP • LS • Minimization of generation costs for the customer and utility • Shiftable load power limits • EV SoC limits • EV availability [122] • PS • Minimization of PAR of the system • Grid power injection limits • EV charging efficiency

### **Table 4.** *Cont.*

• EV SoC limits








### **Table 4.** *Cont.*

### **9. Discussion and Findings**

During the systematic review of the papers as a part of the literature survey, several research gaps were identified in the present research scenario, as well as implementations in various projects across the research domain. Some of the key findings identified during the survey include:


tion of different DG aggregating companies to make DG-DSM integration into the commercial markets more profitable and easier to implement on a technical front.

