Battery Storage for the Utility-Scale Distributed Photovoltaic Generations

Nor, Nursyarizal Mohd; Ali, Abid; Ibrahim, Taib; Romlie, Mohd Fakhizan

doi:10.1109/access.2017.2778004

Cited by 48 publications

(23 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies discussing the optimal dispatch for distribution system problems use metaheuristics such as algorithm and as an approach technique. A genetic algorithm is used in [12] for optimal DG allocation and sizing; in [13], for optimal location and sizing of distributed PV generations, with battery storage systems; in [14], it is applied to a fuzzy-based model, to approach the uncertainty and load variability problem. The stochastic nature of the PV generation penetration problem is also treated in [15], which proposes a stochastic power flow and a solution from a probabilistic model.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

et al. 2019

View full text Add to dashboard Cite

The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

et al. 2019

View full text Add to dashboard Cite

show abstract

“…iii. If the hourly active power selling price to the market (λ P,sell,t ) is higher than the price of ESS discharging (λ ESS,Dis ) (i.e., 70% of the utmost value of active power selling price [9], [42]), and SOC t is more than SOC min , then the bat defines ESS mode as a discharging state. Then, it generates a negative random number for P ESS within the ESS discharging power limits, P ESS ∈ {0, P max,Dis }, at the hour t of the day.…”

Section: B Solution Algorithm Of the Mg Operation Problemmentioning

confidence: 99%

A Framework for Profit Maximization in a Grid-Connected Microgrid With Hybrid Resources Using a Novel Rule Base-BAT Algorithm

et al. 2020

View full text Add to dashboard Cite

In this paper, an energy management system (EMS) is proposed for optimal operation of a microgrid (MG). Dispersed photovoltaic arrays (PVs) and wind turbine generators (WTs) as renewable energy sources (RES) supply a major part of the network demanded energy. Also, an energy storage system (ESS), a micro-turbine unit (MT), and a fuel cell unit (FC) are integrated. The uncertainty and stochastic nature of the network load and RES data are treated via probabilistic modeling and scenarioselection approach. The predicted day-ahead data of the most diverse hourly scenarios are entered into the proposed EMS to determine the active and reactive power (P-Q) participations of local distributed resources. Likewise, it specifies the discharging/charging power and state of the ESS in addition to the exchanged active/reactive power amounts with the main network. The main goal is to maximize the profit of the secondary grid while satisfying all technical constraints. In the proposed EMS, the day-ahead energy management is developed as a comprehensive optimization problem. Moreover, the paper proposes novel modifications to improve the BAT optimization technique. The optimization problem of the energy management in the microgrid is implemented using a new integrated rule base-improved BAT method. Furthermore, the proposed EMS competence is proven by comparing its performance to recent literature.

show abstract

“…For each hour, each bat in the population adjusts the charging and discharging mode of ESS, using the next rules: a) If the purchasing price of active power from the market ( , , ) is smaller than the limit of ESS charging price (0.4 of the highest purchasing price of active power from the market [26]) then bat sets the ESS mode for charging state and produces a positive random amount of the ESS power in this range {PCh,max, 0}, where PCh,max is the maximum ESS power of charge. b) If the purchasing price of active power from the market ( , , ) is above the limit of ESS charging price, and ESS state of charge at time t (SOCt) is less than the maximum limit (SOCmax), and the full renewable DERs power generation is greater than the full MG load power, then the bat sets the mode of ESS as a charging mode and adjusts the power of ESS as a positive random amount in the range of difference between the full renewable DERs power generation and the total demand load of MG. c) If the sale price of active power of the market ( ,(, ) is above the ESS discharging price limit (0.7 of the highest sale price of active power of the market [26]) and SOCt is less than the minimum limit (SOCmin), then the bat sets the mode of ESS for discharging and produces a negative random amount of the ESS power in this range {0, PDis,max}, where PDis,max is the maximum ESS power of discharge . 5.…”

Section: Solution Algorithmmentioning

confidence: 99%

Optimal operation of a hybrid-energy microgrid with energy storage system

et al. 2019

View full text Add to dashboard Cite

In this paper, a day-ahead profit-maximizing energy management scheme for a grid-tied microgrid operation is proposed. The microgrid contains various types of distributed energy resources (DERs) and an inverter-interfaced battery-bank storage system. The average of day-ahead hourly forecasted data for loads, wind speed, and solar radiation are inputted into the framework of energy management (EMF). To optimize the microgrid performance, EMF determines the hourly dispatch of reactive and active power for each DER. Also, it specifies the discharging and charging times of the energy storage system and the onload tap changer position setting of the transformer connected to the main grid. The main aim is to maximize the revenue of microgrid meeting all technical limitations. The main grid can sell/buy reactive and active powers to/from the microgrid with a variable daily energy price of the market. A collective rule base-BAT algorithm is implemented as a solver of the energy management optimization problem for a grid-tided microgrid. Furthermore, the ability of the suggested EMF is proved in comparison with recent approaches.

show abstract

Battery Storage for the Utility-Scale Distributed Photovoltaic Generations

Cited by 48 publications

References 56 publications

Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers

A Framework for Profit Maximization in a Grid-Connected Microgrid With Hybrid Resources Using a Novel Rule Base-BAT Algorithm

Optimal operation of a hybrid-energy microgrid with energy storage system

Contact Info

Product

Resources

About