An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

Abstract

Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

Day-Ahead Economic Dispatch Strategy for Distribution Network Considering Total Cost Price-Based Demand Response

“…A demand response strategy for controllable loads and EV charging loads is proposed to optimize user electricity costs and stabilize power system load fluctuations (Liu et al, 2018). Ali S. proposed a user power mode scheduling method based on real-time price (RTP) signals, which aims to reduce power cost, peak-to-average ratio, and carbon emissions, and improve user comfort (Ali et al, 2021). Gazafroudi A. S. Amin Shokri.…”

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

“…For example, authors [38] proposed unique normalized reasoning fuzzy neural adaptive (NRFNA) management technique for a double-stage grid-coupled PV system. In [39], a hybrid genetic ant colony optimization (HGACO) algorithm is developed to resolve the programming model for three scenarios: without PV system, with a PV system, and with PV/BES system. The paper [40] developed an energy optimization model by the combination of the genetic algorithm with game theorybased fuzzy logic.…”

Performance on Power, Hot and Cold Water Generation of a Hybrid Photovoltaic Thermal Module

“…In some cases, for example, use of PV-air conditioner might reduce over 75% of grid electrical power for airconditioning in office building during daytime [1][2][3]. However, in hot country such as Thailand, the PV module temperature might reach 70 o C or more in the afternoon then the electrical efficiency was reduced significantly and only 75% or less of the power at standard condition could be generated [4][5][6][7], then many approaches to control the module temperature were presented. Sun et al [8] and Biwole et al [9,10] used phase change material (PCM) such as paraffin embedded at the rear surface of PV module and the module temperature was controlled and approaching to the PCM melting point during daytime.…”