*1.4. Suggested Solutions in DSM Implementation*

The following suggested solutions are viable for implementation and for driving grid integration programs in a more effective and coordinated way to deal with the above concerns and obstacles faced during the implementation of various policies concerning DSM using DGs systems:


### *1.5. Outline of This Paper*

This paper is presented to address the issues and solutions of DSM using a methodological and critical survey-based exploration of the implementation of DSM using DERs. Efforts are made to put forth the following points concisely: Firstly, to assess and study the suggested optimization techniques and implementations of DSM in the present literature. This will allow the researchers with the necessary exposure to arrive at more practical and better optimization techniques to establish a proper energy management system (EMS). In addition, energy management modeling studies are examined in terms of uncertainty modeling techniques, objective functions, constraints, and optimization techniques. Lastly, EMS-related papers are reviewed and analyzed correctly to help the researcher find out the problems and the solutions.

The remaining part of this review article is designed as follows: Section 2 represents the detailed review methodology used to formulate this paper, Section 3 states the brief introduction to DSM and DR, Section 4 briefly states the various DGs possible in an intelligent grid network, Section 5 represents the DSM with different types of cleaner energy, Section 6 briefly illustrates the energy management system and some standards related to DG integration, Section 7 explains some issues related to different types of DGs being integrated with DSM techniques, Section 8 represents the various optimization techniques ascribed to DSM with objective and objective functions, Section 9 deals with the different research gaps and critical analyses, and the future scope and conclusion are analyzed in Sections 10 and 11, respectively.

### **2. Review Methodology**

Any research project's primary focus is on three key elements: the purpose, study technique, and outcome, as well as future implementation prospects. An approach based on an analytic-based search technique was undertaken on numerous scientific and interpretive sources such as Google Scholar, ResearchGate, IEEE Explorer, and Scopus to gain a detailed and complete overview of existing research publications. Combinations of thematic words, such as "Demand-side management distributed energy resources", "Demand response", "Energy management using distributed energy sources", "Optimization", "Scheduling", "Distributed energy sources integration in microgrids", and so on, were used to filter out the critical articles using search engines. Specific search engine parameters were employed to find relevant, on-point, particular research papers for the review study. Exact keywords, peer-reviewed publications published in English mainly in the last ten years, and openaccess articles were the deciding factors.

Based on the research articles, an eight-point prospect was developed:


As shown in Figure 1, 31 review papers, 40 case studies, 15 news articles, 107 technical papers, and 10 research reports were reviewed and placed in this paper.

**Figure 1.** Review methodology for this paper.

### **3. Demand-Side Management**

Demand-side management is an essential part of an intelligent grid architecture because it allows consumers to adjust their load consumption patterns, making it a critical feature of an energy management system in power delivery networks [11,12]. "The planning, implementation, and monitoring of those daily activities designed to influence customer use of electricity in ways that will produce desired changes in the utility's load shape, i.e., time pattern and magnitude of a utility's load," according to the Electric Power Research Institute (EPRI) [13]. Instead of relying on additional generation to meet demand, DSM prioritizes the integration of power-saving techniques, the implementation of variable or dynamic unit pricing, and the adoption of DR-based programs to minimize peak load, managing the DGs to establish a proper power balance, as shown in Figure 2.

**Figure 2.** Principle of DSM in the smart grid environment.

The four methodologies outlined below and illustrated in Figure 3 can be used to classify various alterations that can be used to shape and define the electricity load profiles:

**Figure 3.** Basic principle of DSM.

*Energy efficiency (EE)*: These are end-user, appliance-specific controls intended to reduce load utilization over time by employing energy-saving methods on the device level. Rather than relying on an event-triggered strategy for consumption profile minimization, energy efficiency refers to the reduction in overall load consumption achieved by providing more efficient power delivery for each unit with respect to the supplied input power to the appliance, decreasing consumption over time. An in-depth look at the energy efficiency improvement profiles, measurements, and roadblocks can be found in [14,15].

*Time of use (ToU)*: The ToU pricing method divides the utility's fixed tariff into 24 h time blocks and then assigns a variable pricing profile for each period [16,17]. This method can help keep peak load rates and seasonal fluctuations in pricing tariffs under control based on the hourly block-based signal tariff of electricity units.

*Spinning reserve*: In the case of a drastic shortfall in generating levels, the spinning reserve is synonymously recognized with the electric power system's backup power, which may be utilized by the distribution network operator (DNO) to balance the difference or gaps between demand and supply in generation [18]. Power outages can be caused by various factors, including damage to producing units, inadequate load prediction, and dispatch scheduling [19]. In general, there are two types of spinning reserves: primary and secondary [16], with the central spinning reserve employing frequency regulation to limit active power output and the secondary spinning reserve injecting extra active power.

*Demand response*: Energy users depart from their usual use patterns in response to unit rate variations over time or incentive programs. The primary focus is to reduce load profiles during critical tariff periods in the energy wholesale market or when grid reliability is uncertain [20]. Short-term variations throughout the day's critical peak pricing/usage times, when demand is low and spinning reserve capacity is scarce, are of primary interest to DR. DSM is more concerned with long-term load profiles, which may be accomplished on the demand side by improving energy efficiency or adopting consumer-centric usage behavior.
