Demand response governed swarm intelligent grid scheduling framework for social welfare

Abstract Peak load defines the generation, transmission and distribution capacity of interconnected power network. As load changes throughout the day and the year, electricity systems must be able to deliver the maximum load at all times, which will be hard trade for a practical power network. Smart grid technologies show strong potential to optimize asset utilization by shifting peak load to off peak times, thereby decoupling the electricity growth from peak load growth. Under Smart grid trade regulation, with continuous varying demand pattern, electricity price will be uneven as well. On this view point, in order to obtain a flatten demand, without affecting the welfare of the market participants, this paper presents an on-going effort to develop Demand Response (DR) governed swarm intelligence based stochastic peak load modeling methodology capable of restoring the market equilibrium during price and demand oscillations of the real-time smart power networks. This proposed DR based methodology allows generators and loads to interact in an automated fashion in real time, coordinating demand to flatten spikes and thereby minimizing erratic variations of price of electricity. For proper utilization of DR connectivity, a Curtailment Limiting Index (CLI) has been formulated, monitoring which in real time, for each of the Load Dispatch Centers (LDCs), the system operator can shape the electricity demand according to the available capacity of generation, transmission and distribution assets. The proposed methodology can also be highlighted for generating the most economical schedule for social welfare with standard operational status in terms of voltage profile, system loss and optimal load curtailment. The case study has been carried out in IEEE 30 bus scenario as well as on a practical 203 bus-265 line power network (Indian Eastern Grid) with both generator characteristics and price responsive demand characteristics or DR as inputs and illustrious Particle Swarm Optimization (PSO) technique has assisted the fusion of the proposed model and methodology. Encouraging simulation results suggest that, the effective deployment of this methodology may lead to an operating condition where an overall benefit of all the power market participants with standard operational status can be ensured and the misuse of electricity will be minimized.