Overview

The Dispatching Algorithm is a critical component in the energy management of smart cities, where it decides when to utilize grid power, battery power, and when to direct energy to the hot water tank. These decisions are based on the current output of Renewable Energy Resources (RER), the battery’s charge level, and the temperature of the hot water tank.

Smart Cities and Energy Efficiency

As smart cities evolve to counter the challenges of urbanization, such as resource scarcity and energy inefficiency, they incorporate advanced technologies to improve the quality of life for their residents. These technologies reduce the workload of city workers by allowing them to focus on strategic initiatives, while independent agents, mobile devices, bots, and sensors handle everyday tasks.

Proposed Dispatching Control Algorithm

The proposed Dispatching Control Algorithm divides the total renewable energy resources into four components:

Q(t)=Q1(t)+Q2(t)+Q3(t)+Q4(t)Q(t) = Q_1(t) + Q_2(t) + Q_3(t) + Q_4(t)Q(t)=Q1​(t)+Q2​(t)+Q3​(t)+Q4​(t)

  • Q1(t)Q_1(t)Q1​(t): Direct energy to the load.
  • Q2(t)Q_2(t)Q2​(t): Energy directed to battery storage.
  • Q3(t)Q_3(t)Q3​(t): Energy directed to thermal storage.
  • Q4(t)Q_4(t)Q4​(t): Curtailment, or excess energy that cannot be utilized.

The algorithm prioritizes maximizing energy sent directly to the load (Q1(t)Q_1(t)Q1​(t)) and thermal storage (Q3(t)Q_3(t)Q3​(t)), while directing any remaining energy to the battery (Q2(t)Q_2(t)Q2​(t)). The energy stored in the battery is updated every hour according to the following equation:

QC(t)=ηB×QC1(t)−QC2(t)QC(t) = \eta_B \times QC_1(t) – QC_2(t)QC(t)=ηB​×QC1​(t)−QC2​(t)

Where:

  • QC(t)QC(t)QC(t): Battery storage level.
  • ηB\eta_BηB​: Charging efficiency.
  • QC1(t)QC_1(t)QC1​(t): Power transmitted from the battery to the load.
  • QC2(t)QC_2(t)QC2​(t): Power received by the battery.

Energy Management for Hot Water Tanks

The same system is used for managing the hot water tank. The maximum storage capacity RmaxR_{max}Rmax​ and charging efficiency ηRB\eta_{RB}ηRB​ are considered, and the energy is managed as follows:

Q5(t)=KG(t)Q_5(t) = KG(t)Q5​(t)=KG(t)

Where:

  • Q5(t)Q_5(t)Q5​(t): Power transmitted from the tank to the load.
  • KG(t)KG(t)KG(t): Load from the tank.

The tank’s energy storage must be maintained above a minimum level, and if RER is insufficient, the grid provides backup energy QH2(t)QH_2(t)QH2​(t) to maintain the tank’s charge.

Meeting Electrical Load Requirements

The electrical load must be met every hour, with grid energy QH1(t)QH_1(t)QH1​(t) used when RER output is low, ensuring that both the battery and the hot water tank remain sufficiently charged.

System Efficiency and Cost Optimization

System efficiency is defined by the fraction of RER used and the curtailment of energy:

Efficiency=RER outputTotal Load\text{Efficiency} = \frac{\text{RER output}}{\text{Total Load}}Efficiency=Total LoadRER output​

The algorithm aims to maximize RER penetration while minimizing curtailment. Performance metrics include curtailment, cost, and the percentage of total load met by RER. The overall annual cost is calculated based on operational and capital costs, which depend on the number of batteries, PV area, tank capacity, and wind turbines.

Smart Cities and Sustainable Development

Smart cities must focus on sustainable development, integrating technologies such as smart grids and water management systems. These systems ensure energy efficiency, resource management, and improved living conditions. The Dispatching Algorithm plays a crucial role in achieving these goals by optimizing energy production, storage, and consumption.

Conclusion

The Dispatching Algorithm effectively manages energy resources in smart cities, ensuring that the electrical load is met efficiently while minimizing costs and maximizing the use of renewable energy. By incorporating storage systems like batteries and thermal tanks, the algorithm enhances the overall energy management strategy, contributing to the development of sustainable and resilient urban environments.