Photovoltaic Energy Storage System Research Articles

Optimization Method of Photovoltaic Microgrid Energy Storage System Based on Price-based DR

Extreme events lead to an increasing number of power outages in the distribution network. It is currently the most effective method to restore power supply after distribution network failure to connect distributed photovoltaic to the distribution network in the form of microgrids. However, the randomness of distributed PV output and load is the biggest obstacle limiting its development. Therefore, an optimization method of photovoltaic microgrid energy storage system (ESS) based on price-based demand response (DR) is proposed in this paper. Firstly, based on the influence of the uncertainty of the time of use (TOU) and load on the price-based DR, a price-based DR model is built. Then, taking the minimum difference between photovoltaic output and load demand as the optimization objective, the optimal operation model of price-based DR based on the fuzzy chance constrained program (FCCP) is established, and an optimization model of photovoltaic microgrid ESS based on FCCP is obtained. Finally, the fuzzy chance optimization problem is converted into a deterministic optimization problem through constraint equivalent treatment. The proposed method is verified by numerical simulations.

Research on improved linear auto-disturbance rejection control of hybrid energy storage in optical storage microgrid

Photovoltaic power generation has uncertainties such as randomness and volatility. In order to ensure the stable operation of the power system, hybrid energy storage technology is introduced to improve the voltage stability of the optical storage microgrid. Traditional PI control still needs some help in response speed and overshoot. Facing the hybrid energy storage microgrid system with nonlinear and strong coupling characteristics, in order to improve power quality and reduce bus voltage fluctuation and impact, this paper designs an improved active disturbance rejection control (ADRC) integrated energy storage control system based on a fuzzy algorithm, Based on the traditional double closed-loop PI control, the fuzzy LADRC is used to replace the voltage outer loop, and the system model is built on the MATLAB/Simulink platform for simulation. The results show that the proposed control strategy has a smaller overshoot, faster dynamic response time, and good anti-interference ability compared with the traditional PI and ADRC, which improves the dynamic performance of the photovoltaic energy storage system while stabilizing the bus voltage fluctuation and ensures the stable operation of the powergrid.

Power control of PV and grid-assisted CAES system integrated into SONATRACH LPG SP1 ELR1 pumping station with thermal energy recovery from flared LPG

The Algerian National Company for the Transport and Marketing of Hydrocarbons (SONATRACH) provides crude oil, natural gas, condensate, and liquefied petroleum gas (LPG), which recorded an average invoiced electricity consumption of 1.3 M$/yr from 2017 to 2022 for Region of Transport Haoud El-Hamra (RTH) alone. LPG SP1 ELR1 (Pumping Station 1 of Expansion Line 1 at Alrar-Hassi R’mel), as one of the RTH pumping stations, indicated a considerable electricity supply through three 220 KV overhead electrical lines, reduced to 5.5 KV using the transformers to ensure a station input power of around 4.5 MW, representing a loss of 21 % of annual consumption. However, the input power is never fully utilized by the station and paid on average 391.6 K$/yr added to equipping the station with transformers at an extra cost of 1.78 M$ and the power outages from the grid resulting in significant losses of 105.6 K$/h in pumped LPG sales. This paper presents the first process study of a photovoltaic (PV) and electrical grid-assisted Compressed Air Energy Storage (CAES) system integrated into the LPG SP1 ELR1 with the flared LPG recovery to achieve electrical independence, and it includes a system analysis on process optimization, cost, and environmental impact. The study discusses a combination of PV, electrical grid, and, in particular, the CAES process simulated using ASPEN HYSYS software to control its output power, including three scenarios: without air thermal energy recovery (ATER), with ATER, and with LPG thermal energy recovery (LPG-TER). According to the results, the CAES with LPG-TER demonstrated a more than 100 % increase in power delivery, corresponding to approximately 10 MW of generated power compared to input power of 4.5 MW, thus exceeding the needs of the pumping station by two times. In addition, the comparison with the literature-mentioned competing systems underscores the system's superiority, with a 32 %, 55 %, and 66–68 % improvement over D-CAES, I-CAES, and A-CAES, respectively. The study revealed that achieving power grid independence through the CAES system resulted in annual capital and operating cost savings of 2.22 M$/yr. Adopting the CAES system can significantly reduce CO2 emissions from natural gas combustion to power the grid, resulting in a 29 % decrease in combined emissions from LPG and natural gas. The study's extended findings to similar SONATRACH pumping stations show a reducing CO2 emissions by 22.17 % and savings of 3679 MW/yr. Beyond SONATARCH, the system offers savings and flaring reduction from 100 MW/day and 5.28 bbl/day up to 42400 MW/day and 2329.5 bbl/day.

A new hybrid CBSA-GA optimization method and MRMI-LSTM forecasting algorithm for PV-ESS planning in distribution networks

Renewable energy sources have gained significant attention as a priority option for cost reduction and emissions reduction from fossil fuels. However, their utilization introduces uncertainty and decreases reliability in distribution networks. To effectively harness these resources and mitigate their uncertainties, this paper proposes a model for determining the optimal size and location of photovoltaic (PV) systems and energy storage systems (ESS) in electrical energy distribution networks. The objective is to minimize network power losses, voltage deviations, customer interruption cost, PV system installation cost, and ESS installation cost. To achieve this, the study employs a hybrid forecasting model based on MRMI feature selection and LSTM deep neural network to forecast daily solar power and electrical load for 12 different days across various months of the year, considering the desired electrical energy distribution system. Subsequently, these predicted values serve as 12 scenarios to determine the optimal location and size of PV systems and ESSs. The optimization problem is tackled using a novel hybrid optimization method known as the Coot Bird Search Algorithm-Genetic Algorithm (CBSA-GA). The efficiency of the proposed algorithm is evaluated by comparing its results with GA, PSO, and CBSA algorithms. Among the optimization methods discussed in this paper, CBSA-GA excels in reducing the objective function by accurately determining the capacities of PV resources and ESS. Compared to scenarios where distributed generation (DG) is not integrated into the distribution network, the implementation of CBSA-GA algorithm results in significant improvements in the objective function for Cases 1 to 3. The reductions achieved in the objective function are 9.10 %, 11.88 %, and 24.39 % for Cases 1, 2, and 3, respectively. These findings highlight the effectiveness of CBSA-GA in optimizing the integration of DG resources, specifically in terms of reducing the objective function and improving system performance.

Two-stage scheduling of smart electric vehicle charging stations and inverter-based Volt-VAR control using a prediction error-integrated deep reinforcement learning method

Smart electric vehicle charging stations (EVCSs) having distributed energy resources (DERs), including photovoltaic (PV) systems and energy storage systems (ESSs), are becoming vital devices for increasing their profit and maintaining stable distribution grid operations by scheduling the real/reactive power of DERs. However, prediction errors of PV generation outputs and electric vehicle (EV) loads from EVCSs may decrease their profit and destabilize the distribution grid owing to incorrect EV charging scheduling and voltage regulation. To address this issue, we propose a two-stage framework for smart EVCS scheduling and inverter-based Volt-VAR control (VVC) using prediction error-integrated deep reinforcement learning (DRL). In the first stage, the EVCS agents train their neural network model with a 30-min resolution to maximize their profit through a day-ahead charging/discharging scheduling of ESSs in the EVCSs while responding to various prediction errors of PV generation outputs and EV loads. The total real power consumption of each EVCS, including the charging/discharging schedules of the ESSs calculated in the first stage, is delivered to the second stage, in which the VVC agent trains its neural network model with a 5-min resolution to minimize the real power loss and voltage violations through real-time reactive power scheduling of the ESSs in the EVCSs via their inverters. The proposed approach was tested in the IEEE 33-node and IEEE 123-node distribution systems. The results show that the proposed approach outperforms DRL methods that do not consider the prediction errors in terms of profitability of the EVCS and reduction of real power loss.

High Performance SiC Power Module Based on Repackaging of Discrete SiC Devices

Increased adoption of electric vehicles (EV), photovoltaic (PV), and battery energy storage systems (BESS) is driving the need for high-current SiC power modules. The state-of-the-art multichip module is substantially more expensive than the IGBT module. This paper proposes a cost-effective packaging methodology for high-power SiC intelligent power modules (IPMs) with discrete SiC devices. The proposed IPM integrates the gate drivers, decoupling capacitors, snubbers, discrete SiC devices, direct bond copper (DBC), base plate, temperature sensors, and overcurrent protection circuits, achieving very low loop inductance and thermal resistance, and more than 50% cost reduction. A 1200V/480A/2.15mΩ half-bridge IPM is presented for comparison with commercial counterparts. A 1200V/240A/4.3mΩ six-phase IPM is developed, and its performance is experimentally verified.

Sizing Optimization of a Photovoltaic Hybrid Energy Storage System Based on Long Time-Series Simulation Considering Battery Life

An energy storage system works in sync with a photovoltaic system to effectively alleviate the intermittency in the photovoltaic output. Owing to its high power density and long life, supercapacitors make the battery–supercapacitor hybrid energy storage system (HESS) a good solution. This study considers the particularity of annual illumination due to climate conditions in Harbin, China. A global optimal PV-HESS sizing method is proposed by constructing a PV-HESS operating cost model and taking the annual system operating cost as the objective function. To consider the effect of battery life degradation due to different charge and discharge rates and charge and discharge times, a semi-empirical model based on the Arrhenius model was used to quantify the battery life degradation. Based on the effects of different seasons and different photovoltaic panel sizes, batteries, and supercapacitors on the optimization results, four scenarios are proposed. The feasibility of the system configuration corresponding to the four scenarios is discussed, and an optimal sizing configuration of the system is obtained. The simulation results show that the proposed method can effectively balance the degradation of the ESS due to irregular charging and discharging and determine the minimum operating cost and a reasonable sizing configuration of the system.

Distributed predictive control design to achieve economically optimal power flow for DC microgrid clusters

AbstractDC microgrid clusters are a collection of interconnected microgrids that allow for flexible power flow, leading to economic benefits and improved resilience from distributed generation. However, managing power flow among interconnected microgrids with different components such as photovoltaic, wind turbine, and battery energy storage systems, as well as various dynamic operation scenarios, presents a significant challenge for proportional‐integral (PI)‐based controllers. To address this challenge, this paper proposes a distributed predictive control design in the hierarchical control paradigm that aims to achieve economically optimal power flow (EOPF) for DC microgrid clusters. The predictive controller considers multiple objectives optimisation, including generation cost models, converter losses, and transmission losses over both local lines and tie‐lines, and design and implementation of the two‐layer tertiary control for the EOPF of DCMGCs are presented. Hardware‐in‐the loop (HIL) experimental results demonstrate the effectiveness of the controller design.

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

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.

Analysis of Photovoltaic Plants with Battery Energy Storage Systems (PV-BESS) for Monthly Constant Power Operation

Photovoltaic generation is one of the key technologies in the production of electricity from renewable sources. However, the intermittent nature of solar radiation poses a challenge to effectively integrate this renewable resource into the electrical power system. The price reduction of battery storage systems in the coming years presents an opportunity for their practical combination with utility-scale photovoltaic plants. The integration of properly sized photovoltaic and battery energy storage systems (PV-BESS) for the delivery of constant power not only guarantees high energy availability, but also enables a possible increase in the number of PV installations and the PV penetration. A massive data analysis with long-term simulations is carried out and indicators of energy unavailability of the combined system are identified to assess the reliability of power production. The proposed indicators allow to determine the appropriate sizing of the battery energy storage system for a utility-scale photovoltaic plant in a planning stage, as well as suggest the recommended operating points made for each month through a set of graphs and indicators. The presence of an inflection zone has been observed, beyond which any increase in storage does not generate significant reductions in the unavailability of energy. This critical zone is considered the sweet spot for the size of the storage, beyond which it is not sensible to increase its size. Identifying the critical point is crucial to determining the optimal storage size. The system is capable of providing reliable supply of constant power in monthly periods while ensuring capacity credit levels above 95%, which increases the penetration of this renewable resource. Despite the fact that the study focuses exclusively on the analysis from an energy perspective, it is important to consider the constraints associated to real storage systems and limit their oversizing.

Coordinated PSO-ANFIS-Based 2 MPPT Control of Microgrid with Solar Photovoltaic and Battery Energy Storage System

The microgrid is a group of smaller renewable energy sources (REs), which act in a coordinated manner to provide the required amount of active power and additional services when required. This article proposes coordinated power management for a microgrid with the integration of solar PV plants with maximum power point tracking (MPPT) to enhance power generation and conversion using a hybrid MPPT method based on particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS) to acquire rapid and maximum PV power along with battery energy storage control to maintain the stable voltage and frequency (V-f) of an isolated microgrid. In addition, it is proposed to provide active and reactive power (P-Q) regulation for the grid connected. The approach used provides more regulation due to the least root mean square error (RMSE), which improves photovoltaic (PV) potential extraction. The comparison results of the PSO-ANFIS and P&O controllers of the MPPT and the controller of the energy storage devices combined with the V-f (or P-Q) controller of the inverter all show effective coordination between the control systems. This is the most important need for contemporary microgrids, considering the potential of changing irradiance in the grid following mode, the grid forming mode under an island scenario, and back-to-grid synchronization. With the test model, the islanded and grid-islanded-grid connected modes are investigated separately. The results demonstrate conclusively that the proposed strategies are effective. To run the simulations, MATLAB and SimPowerSystems are utilized.

A new MPPT design using arithmetic optimization algorithm for PV energy storage systems operating under partial shading conditions

Arithmetic optimization is a new metaheuristic algorithm that has shown great strength and good performance in handling and solving complex problems. In photovoltaic systems, and especially in the case of a partial shading condition, the power-voltage curve of the photovoltaic array exhibits multiple peaks of which only one is global, making the tracking of the global maximum power point more complex, and thus requiring a powerful metaheuristic maximum power point tracking algorithm. Therefore, to address this problem, this paper introduces a new design of an arithmetic optimization based maximum power point tracking controller for battery charging schemes through photovoltaic systems operating under partial shading conditions. The main objective of the arithmetic optimization based maximum power point tracking proposed here is to maximize the power extracted from the photovoltaic array under all types of climatic conditions in order to efficiently charge the battery through the maximization of its charging current. The feasibility and effectiveness of the proposed arithmetic optimization based maximum power point tracking method are verified through MATLAB simulations and processor-in-the-loop test using the low-cost STM32F429 embedded board. Moreover, a comparison of its performance with existing maximum power point tracking methods such as, hybrid of grey wolf optimization and particle swarm optimization, particle swarm optimization and perturb and observe is also carried out while considering real climatic conditions as well as uniform and partial shading scenarios. Simulation and processor-in-the-loop results have demonstrated the good performance of the proposed method in terms of battery charging efficiency as well as the ability to track the global maximum power point with an efficiency of 99.99% and a tracking time of 0.85 s.

Economic Feasibility of Hybrid Solar-Powered Charging Station with Battery Energy Storage System in Thailand

Developing a public charging infrastructure is essential for the promotion of electric cars (EVs), especially in developing countries. The use of renewable energy sources (RESs), especially solar and the replacement of fossil fuels in EV charging stations has the potential to improve economic efficiency while significantly lowering greenhouse gas emissions and improving urban air quality. Therefore, the purpose of this paper is to investigate the economic feasibility of a hybrid solar photovoltaic (PV) and battery energy storage system (BESS) for environmentally friendly EV charging stations in a university campus under different EV charger utilization rates, electricity costs, and charging types. The results showed that installing a level 2 solar PV charging station at the current subsidized rate provides the most economic benefits, while installing BESS for peak shaving is the least profitable due to the high cost. The sensitivity analysis also revealed that if the cost of the BESS decreases, the IRR of the project will increase. This study aims to promote the development of technologically and environmentally feasible EV charging stations powered by RESs.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.

Event Detection, Classification and Localization in an Active Distribution Grid Using Data-Driven System Identification, Weighted Voting and Graph

Event detection, classification, and localization (ED-C-L) in an active distribution network are challenging given system dynamics but critical for taking the control actions to mitigate the impact on the system operation. In this work, a new set of algorithms are developed for ED-C-L including, a) prony based dynamic window and event detection, b) data-driven system identification (DD-SI) using Koopman Mode analysis (KMA), physics-rule, and weighted-vote based assembling technique for event classification, and c) weighted graph and KMA based localization using distribution phasor measurement unit (D-PMU) data. DD-SI is utilized to construct a transient energy matrix (TEM) and capture the system dynamics using the temporal and spatial signature of events without using a labeled dataset. The proposed technique considers system dynamics for ED-C-L to achieve enhanced accuracy, compared to most of the existing data-driven techniques. A lab-scale real-time testbed is developed to generate D-PMU data utilizing a 24 bus distribution system simulated in the OPAL-RT/Hypersim for evaluating the performance of the proposed algorithm, and measured using selected metrics. Case scenarios consider utility-scale solar photovoltaic (PV) and battery energy storage system (BESS) integration at dispersed locations to validate proposed algorithms under dynamically changing operational scenarios. Further, the developed approach is applied on DPMU data from real distribution systems with DERs to showcase the superiority of the proposed approach to field systems.

Research on coordinated control strategy of photovoltaic energy storage system

In this paper, the modular design is adopted to study the control strategy of photovoltaic system, energy storage system and flexible DC system, so as to achieve the design and control strategy research of the whole system of “photovoltaic + energy storage + DC + flexible DC”. This realizes the flexibility and diversity of networking. Due to space reasons, this article focuses on the detailed explanation of the photovoltaic energy storage system control strategy, including the maximum power tracking control strategy of photovoltaic power generation, photovoltaic power generation boost chopper circuit control strategy, photovoltaic power generation DC/AC converter control strategy, PCS device control strategy, PCS virtual impedance control algorithm, etc. Simulation experiments are carried out according to the specific case setting parameters. The simulation results prove that the proposed flexible DC system coordinated control strategy can ensure grid frequency stability and grid voltage stability, and improve the consumption capacity of distributed new energy.

A Robust CCS Predictive Current Control for Photovoltaic Energy Storage System Based on a Nonlinear Disturbance Observer

This article introduces a new control strategy for a bidirectional DC/DC converter used in photovoltaic energy storage systems (PV-ESSs), aimed to address the DC bus voltage deviation problem. The purpose of this paper is to design and practically implement a robust continuous control set predictive current control (CCS-PCC) method based on a nonlinear disturbance observer (NDO) to tightly regulate the common DC bus voltage in islanded DC microgrids. The CCS-PCC controller is used to replace the current loop PI controller, which effectively overcomes the shortcomings of the traditional linear lag control and improves the dynamic performance of the system. At the same time, the CCS-PCC method solves the inherent defect of finite control set predictive current control (FCS-PCC), where the switching frequency is not fixed. It effectively reduces the current ripple and realizes constant frequency control. In order to effectively suppress the DC bus voltage fluctuation during transients, the nonlinear disturbance observer is designed and combined with CCS-PCC. The feedforward method based on the NDO was utilized to enhance the disturbance rejection capability of the system. The simulation and experimental results show the feasibility and effectiveness of the proposed CCS-PCC+NDO approach, both during transient and steady-state operating conditions.

A Two-Switch SEPIC-Based Nonisolated Three-Port Converter Featuring High Step-Up Gain for Solar PV Applications

A two-switch nonisolated DC–DC three-port converter (TPC) based on a modified SEPIC circuit with a high voltage gain is introduced and a unified power control strategy is proposed in this article. The proposed TPC is integrated with one low-voltage input and one high-voltage port and is suitable for photovoltaic (PV) applications. The operating principle of this TPC is analyzed in detail and then its static voltage gain is determined. In addition, the advantage of using a modified SEPIC circuit is that fewer components are employed compared to a similar topology. Furthermore, the state space method is adopted to analyze this multiple-input multiple-output (MIMO) system and then a small signal model is built to design an appropriate control strategy. Finally, the proposed 100 W TPC connects a solar PV energy storage system to a load demonstrating efficiency ranging from 85% to 94%. Moreover, the steady- and dynamic-state performances of this TPC are verified in the SimStar MT3200 HIL platform, which illustrates the feasibility of the proposed TPC and its power control strategy for solar PV applications.

A novel prediction and control method for solar energy dispatch based on the battery energy storage system using an experimental dataset

The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE% =1.68 %.

A Novel High Gain Non-Isolated Three-Port DC–DC Converter for DC Microgrid Applications

In this paper, a high gain nonisolated three-port bidirectional DC–DC converter is proposed to interface solar photovoltaic and battery energy storage system to DC bus with a reduced number of components. Four modes of operation based on the power flow and load demand are identified. The operating principle of the proposed converter and its operational waveform for all four modes of operation is described in this paper. The steady-state analysis of the proposed converter is performed to determine the voltage gain of the converter in all four modes of operation. The steady-state analysis is done for both continuous conduction mode and discontinuous conduction mode; and the boundary condition. The reduced number of components to achieve high-voltage gain ensures the lowering of the cost and weight of the converter. The analytical results are validated utilizing the simulation results from PSCAD and hardware results. Also, the results indicate that the ripple in the current and voltage is reduced significantly.