Storage capacity allocation strategy for distribution network with distributed photovoltaic generators

Li, Bin; Li, Xingchen; Bai, Xiaoqing; Li, Zhineng

doi:10.1007/s40565-018-0429-x

Cited by 20 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…References [3] and [8] use energy storage system (ESS) to address the variability of RES by matching the load profile. The optimal size and operation strategies of ESS are discussed in [8] - [10]. Scenario-based stochastic optimization technique has been used to model the uncertainty of wind power in [11], [12].…”

Section: Cumulative Density Functionmentioning

confidence: 99%

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

Singla

Singh

Yadav

2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

This paper proposes a simple and practical approach to model the uncertainty of solar irradiance and determines the optimized day-ahead (DA) schedule of electricity market. The problem formulation incorporates the power output of distributed solar photovoltaic generator (DSPVG) and forecasted load demands with a specified level of certainty. The proposed approach determines the certainty levels of the random variables (solar irradiance and forecasted load demand) from their probability density function curves. In this process of optimization, the energy storage system (ESS) has also been modeled based on the fact that the energy stored during low locational marginal price (LMP) periods and dispatched during high LMP periods would strengthen the economy of DA schedule. The objective of the formulated non-linear optimization problem is to maximize the social welfare of market participants, which incorporates the assured generation outputs of DSPVG, subject to real and reactive power balance and transmission capability constraints of the system and charging/discharging and energy storage constraints of ESS. The simulation has been performed on the Indian utility 62-bus system. The results are presented with a large number of cases to demonstrate the effectiveness of the proposed approach for the efficient, economic and reliable operation of DA electricity markets.

show abstract

Section: Cumulative Density Functionmentioning

confidence: 99%

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

Singla

Singh

Yadav

2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…Besides, in order to introduce a larger number of RESs into the power grid, the deployment of energy storage systems (ESSs) becomes a key point for the purpose of compensating the production intermittency, as well as for enhancing the profitability of renewable power plants [6]- [9]. There exist a wide range of storage technologies, which could be divided into capacity-oriented storage technologies (such as pumped hydroelectric or hydrogen storage systems) and access-oriented technologies (such as batteries, flywheels, and supercapacitors).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

Martinez-Rico

Zulueta

Argandoña

et al. 2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Considering the increasing integration of renewable energies into the power grid, batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources. Besides, the deployment of batteries can increase the benefits of a renewable power plant. One way to increase the profits with batteries studied in this paper is performing energy arbitrage. This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high. In this paper, a hybrid renewable energy system consisting of wind and solar power with batteries is studied, and an optimization process is conducted in order to maximize the benefits regarding the dayahead production scheduling of the plant. A multi-objective cost function is proposed, which, on the one hand, maximizes the obtained profit, and, on the other hand, reduces the loss of value of the battery. A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective function. With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value, two more simplified cost functions are proposed. Results show the importance of including the energy efficiency in the cost function to optimize. Besides, it is proven that the battery lifetime increases substantially by using the multi-objective cost function, whereas the profitability is similar to the one obtained in case the loss of value is not considered. Finally, due to the small difference in price among hours in the analyzed Iberian electricity market, it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

show abstract

“…Traditional solutions are generally focused on the supply side, and the main solutions are adjusting the reactive power of the PV inverter [4], installing reactive-load compensation equipment such as a reactor [5], and calling energy storage equipment to absorb the extra PV power [6,7]. If necessary, the measure of removing part of active power, namely, "waste of PV power," must be taken [8].…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Voltage Control Based on Coordinated Optimization of PV and Air-Conditioning

Wang

Lü

Dou

et al. 2018

International Journal of Photoenergy

View full text Add to dashboard Cite

This paper proposes a coordinated optimal control strategy of PV generators and air-conditioning loads, in order to handle the possible voltage beyond limit issues resulting from high penetration of PV generators in the distribution networks. This strategy is achieved via coordinately considering the node voltage sensitivity and the adjustment-compensation bid model, to improve the economy of the whole system. As a result, the shortage of the PVs' reactive power control capability is compensated by the adjustable air-conditioning loads, so that the waste of the photovoltaic power can be reduced or even avoided. The case study using an IEEE standard 33-node system, which is further updated with the installation of 5 PV generators and 5 air-conditioning loads, validates the correctness and effectiveness of the proposed control strategy.

show abstract

Storage capacity allocation strategy for distribution network with distributed photovoltaic generators

Cited by 20 publications

References 12 publications

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

Distribution Network Voltage Control Based on Coordinated Optimization of PV and Air-Conditioning

Contact Info

Product

Resources

About