Abstract
The concept of demand side management (DSM) must be emphasized in the current era, foreseeing the drastic escalation of energy demand and depleting fossil fuels. DSM ensures effective facility utilization by controlling clients' energy resources and carefully adjusting their energy demand profiles. In this study, the day-ahead load-shifting DSM technique is modeled as a minimization problem to achieve better load profile. The whale optimization algorithm (WOA) is provided for resolving the DSM optimization problem. The proposed work has been tested on three demand zones with various controllable loads: residential, commercial, and industrial. With the suggested WOA method, a comparison analysis based on peak demand and operational expenses was made. Finally, it is demonstrated that in the different load sectors, the DSM strategy based on the WOA technique provides better cost savings and also helps in achieving flat load profile while reducing peak to average ratio (PAR).
Optimization Techniques for Hybrid Power Systems
TopIntroduction
The term “power grid” has historically been used to refer to the electrical grid that supports all or most of the given functions: Electricity power generation, transmission, distribution, and control (Dileep et al., 2020). The traditional power grid networks are designated to transmit and distribute electricity, originated from synchronously operating conventional energy sources to interconnected consumers. The Smart Grid (SG) in contrast, leverages two-way power transfers and information transfers, to build automated, distributed, and secured energy networks. The conventional power grid and the SG are briefly compared in Table 1 below (Mollah et al., 2020).
Table 1. Traditional grid and smart grid comparison
| Traditional Power Grid | Smart Grid |
| Electromechanical | Digital |
| Single directional communication | Bidirectional communication |
| Less customer preferences | Several customer preferences |
| Physical monitoring | Self-monitoring |
| Centralized power generation | Distributed power generation |
| Faults and blackouts | Adaptive and islanding |
| Less number of sensors | Sensors throughout the system |
| Control is limited to some extent | Universal control |
| Manual restoration from threats | Self-healing |
The smart grid (SG) is a vision of the future power systems incorporating innovative sensor technologies, control techniques and communication technologies. The major adorned features of the SG system include abilities viz. hack-proof, self-healing, consumer-friendly, bi-directional interactive communication, energy market-based effective operation, high power quality and finest assets (Gunduz et al., 2020). Numerous economic, political, environmental, social, and technical aspects are mandates for the proposed modern SG. The SG can supply electricity more effectively and shall react to a wide range of circumstances and events by using contemporary information technology (Kimani et al., 2019).
Key Terms in this Chapter
Self-Healing: When referring to a smart grid, the term “self-healing” describes the grid's capability to autonomously identify, isolate, and repair defects or disruptions without the need for human involvement. In order to improve the overall dependability and resilience of the electrical distribution system, it is a crucial component of contemporary smart grids.
Smart Grid: A modern electrical grid system known as a “smart grid” uses intelligent devices, communication networks, and modern technology to improve the efficiency, dependability, and sustainability of power generation, distribution, and consumption.
Peak Load: Peak load, often referred to as peak demand, is the maximum amount of electricity that a power system or electrical grid may use in a certain period, usually a day or a year. It reflects the maximum quantity of power that all users of the system will ever demand.
Peak to Average Ratio (PAR): The ratio between the peak value and the average value of an electrical signal or waveform is known as the peak-to-average ratio (PAR). It is frequently used to estimate how much an electrical signal's amplitude can vary.
Distributed Generation: The decentralized production of electricity near or close to the point of consumption is referred to as distributed generation (DG). Distributed generation systems create power locally, frequently utilizing smaller-scale renewable energy sources and combined heat and power (CHP) technologies, as opposed to simply depending on huge, centralized power plants to generate electricity and transport it over vast distances through the grid.
Controllable Load: An electrical load or demand that may be purposefully changed or controlled by grid operators, utilities, or customers in response to variations in the availability of power, system conditions, or market signals is referred to as a controllable load. Controllable loads may often be controlled, and their consumption can be changed to match the available power generation or to improve grid performance.
Optimization: The term “optimization” refers to the act of selecting the ideal result or outcome from a range of potential outcomes, given a set of limitations or goals. It is a fundamental idea that is applied in many disciplines, including operations research, computer science, economics, mathematics, and engineering.
DSM Aggregator: A Demand Response (DR) or DSM (Demand-Side Management) aggregator is a specialized organization that stands between power users and grid operators or utilities. In order to participate in demand response programs and energy markets, a DSM aggregator's main responsibility is to pool the energy resources and demand flexibility of many users and manage these resources as a single entity.
Average Load: In relation to electrical power systems, the term “average load” refers to the average or mean amount of electricity consumption for a given period. It indicates the average quantity of electricity used by users at that period.