Optimal Demand Response in a Residential PV Storage System Using Energy Pricing Limits

Manojkumar, Rampelli; Kumar, Chandan; Ganguly, Sanjib

doi:10.1109/tii.2021.3103014

Cited by 18 publications

(4 citation statements)

References 33 publications

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“…Methods that have been mentioned above are originated by using ILP, LP, MILP and OSR but are complex in handling a large number of appliances and their parameters. Further-more, mathematical optimization techniques perform better in modelling, but for implementation point of view they are very complex [14]. Therefore, evolutionary algorithm such as GA performs well in this situation as it tackles all the heuristics of the problem in a better way.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

Rashid

2024

SJET

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A next-generation electrical power system known as the "Smart Grid" (SG) uses two-way communication to generate, use, and transport electrical energy. Demand Response (DR) is one of the SG's primary features (DR). Smart meters in DR transmit a pricing signal to the consumer, allowing them to adjust their demand in reaction to the corresponding price signals. Day-Ahead and Time-of-Use are the most widely used load scheduling schemes, however, they deviate from the Real time pricing scheme (RTP). Due to its erratic nature, integrating renewable energy sources like solar and wind is a difficult process in SG. Because of the fluctuations in both energy consumption patterns and power rates, the majority of current methods for managing demand are predicated either on day-ahead or time-of-use pricing rather than real-time pricing. This study describes a load scheduling system that uses a Genetic Algorithm (GA) to classify various users according to their energy use in a real-time pricing environment. Our load scheduling problem is formulated utilizing the knapsack mathematical formulation technique to reduce the electricity expenditure. In order to keep the grid stable and reduce costs, renewable energy is integrated with the grid's energy to lower the Peak-to-Average Ratio (PAR). The efficiency of the suggested algorithm in terms of electricity cost and PAR reduction is supported by simulation results.

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Section: Related Workmentioning

confidence: 99%

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

Rashid

2024

SJET

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“…In [49], the optimal PV-BES system sizing using rule-based techniques is performed using PSO; however, it resulted in annual power dumps. Similarly, in [50][51][52], the rule-based techniques are integrated with optimization approaches for energy management applications such as peak shaving and demand response using PV-BES. However, the optimal sizing of the PV-BES system has not been explored.…”

Section: Existing Work and Their Limitationsmentioning

confidence: 99%

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

et al. 2023

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In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated with respect to rooftop space and feed-in tariff rates. The battery scheduling is performed using a proposed rule-based energy management strategy. The rules are formulated based on the demand limit, PV export power limit, and state of charge of the battery. Furthermore, optimization modeling with initial choices of parameters and constraints in terms of solar photovoltaic and battery energy storage capabilities is developed to minimize the total net present cost. The hourly values of solar irradiance, air temperature, electrical loads, and electricity rates are considered the inputs of the optimization process. The optimization results are achieved using particle swarm optimization and validated through an uncertainty analysis. It is observed that an optimal photovoltaic and battery energy storage system can reduce the cost of electricity by 12.33%, including the sale of 5944.029 kWh of electricity to the grid. Furthermore, energy consumption, peak demand, and greenhouse gas emissions are reduced by 13.71%, 5.85%, and 62.59%, respectively. A comprehensive analysis between the variable and fixed data for the load, energy from PV, batteries, and the grid, and costs demonstrates that the optimal sizing of photovoltaic and battery energy storage systems with the best mix of energy from PV, batteries, and the grid provides the optimal solution for the proposed configuration.

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“…Reference [26] provides an overview of electrical energy storage technologies, materials, systems, challenges, and prospects for large-scale network storage. Reference [27] proposed an optimal demand response control for a residential photovoltaic (PV) storage system using rulebased energy pricing constraints to minimize system energy costs. Reference [28] discusses the effect of battery energy storage systems on the stability of distribution networks with high penetration levels of inverter-based distributed generation.…”

Section: Introductionmentioning

confidence: 99%

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

et al. 2023

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The use of energy storage power plants (ESPP) seems necessary to create flexibility in the operation of smart grids and increase economic benefits. The power storage power plants connect directly to the smart distribution substation (DS), saving energy costs and increasing energy efficiency. Also, demand response (DR) program management has improved the performance of smart grids by shifting loads. In this regard, the main challenge is the optimal coordination between these two problems by considering control challenges such as limiting the number of charge/discharge times and the number of hours of DR implementation using a linear model that can be solved with powerful commercial solvers. In this paper, we proposed an economic mixed-integer linear programming (MILP) model for optimal coordination of DR and large-scale ESPP considering the practical limitations of charge/discharge times and DR action times with optimal management of distributed generation (DG) resources, which provides more realistic results due to the constraints in operating times in both DR and ESPP. To validate and analyze the proposed model, a standard 33-bus distribution network with a standard Vanadium redox battery power plant and a large 874-bus system with three Vanadium redox battery power plants are considered. Various scenarios have been considered to demonstrate the constraints imposed on the demand side management program and battery charge and discharge, the results of which show that these constraints have a significant impact on the objective function of the problem. Also, by comparing the proposed method with other methods, it was found that the proposed method is more efficient in improving the objective function and limiting options. INDEX TERMS Energy Storage power plant, Distribution Substation, Demand response program, Mathematica model.

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Optimal Demand Response in a Residential PV Storage System Using Energy Pricing Limits

Cited by 18 publications

References 33 publications

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

A Grid-Connected Optimal Hybrid PV-BES System Sizing for Malaysian Commercial Buildings

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

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