Research on Virtual DC Generator-Based Control Strategy of DCMicrogrid with Photovoltaic and Energy Storage

With the penetration of a large number of photovoltaic power generation units and power electronic converters,the DC microgrid shows low inertia characteristics,which might affect the stable operation of the microgrid in extreme cases.In order to enhance the“flexible features”of the interface converter connected to the DC bus,a control strategy of DCmicrogrid with photovoltaic and energy storage based on the virtual DC generator(VDCG)is proposed in this paper.The interface converters of the photovoltaic power generation system and the energy storage system simulates the inertia and damping characteristics of the DC generator to improve the stability of the DC bus voltage.The impedance ratio of DC microgrid was obtained by establishing the small-signal model of photovoltaic power generation system and energy storage system,and the Nyquist curves was applied to analyze the small-signal stability of the system.Finally,the simulation results were verified with MATLAB/Simulink.The results show that the proposed control strategy can slow down the fluctuation of bus voltage under the conditions of photovoltaic power fluctuation and load mutation,thus enhancing the system stability.

Battery-Free Power Supply for Wireless Sensor Combining Photovoltaic Cells and Supercapacitors

This article exhibits the sizing, modelling, and characterization of a power supply (output 3.3 V, 200 mA max, 11 days full autonomy) dedicated to powering a wireless sensor node without a battery but usable as simply as with a battery. This system is modular for various light levels (indoor and outdoor). It is easily integrable into a sensor node, using only commercial circuits. The choices of the photovoltaic surface (amorphous silicon, η 5%, 35 cm2) and of the supercapacitors value (2x 25F, 2.7 V) are explained for permanent operation, considering the solar potential and the consumption. An original part of the paper is devoted to the issue of the startup, in which we demonstrate that after a particular preload, once installed, the device can start on request at the desired time (within 15 days) using as a trigger any light source, such as the LED of a mobile phone.

Distributed Virtual Inertia Based Control of Multiple Photovoltaic Systems in Autonomous Microgrid

The large inertia of a traditional power system slows down system's frequency response but also allows decent time for controlling the system.Since an autonomous renewable microgrid usually has much smaller inertia,the control system must be very fast and accurate to fight against the small inertia and uncertainties.To reduce the demanding requirements on control,this paper proposes to increase the inertia of photovoltaic(PV) system through inertia emulation.The inertia emulation is realized by controlling the charging/discharging of the direct current(DC)-link capacitor over a certain range and adjusting the PV generation when it is feasible and/or necessary.By well designing the inertia,the DC-link capacitor parameters and the control range,the negative impact of inertia emulation on energy efficiency can be reduced.The proposed algorithm can be integrated with distributed generation setting algorithms to improve dynamic performance and lower implementation requirements.Simulation studies demonstrate the effectiveness of the proposed solution.

A novel coordinated control strategy considering power smoothing for a hybrid photovoltaic/battery energy storage system

This work presents a novel coordinated control strategy of a hybrid photovoltaic/battery energy storage(PV/BES) system. Different controller operation modes are simulated considering normal, high fluctuation and emergency conditions. When the system is grid-connected, BES regulates the fluctuated power output which ensures smooth net injected power from the PV/BES system. In islanded operation, BES system is transferred to single master operation during which the frequency and voltage of the islanded microgrid are regulated at the desired level. PSCAD/EMTDC simulation validates the proposed method and obtained favorable results on power set-point tracking strategies with very small deviations of net output power compared to the power set-point. The state-of-charge regulation scheme also very effective with SOC has been regulated between 32% and 79% range.

Optimal configuration for photovoltaic storage system capacity in 5G base station microgrids

Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations.In this study,the idle space of the base station’s energy storage is used to stabilize the photovoltaic output,and a photovoltaic storage system microgrid of a 5G base station is constructed.Aiming at the capacity planning problem of photovoltaic storage systems,a two-layer optimal configuration method is proposed.The inner layer optimization considers the energy sharing among the base station microgrids,combines the communication characteristics of the 5G base station and the backup power demand of the energy storage battery,and determines an economic scheduling strategy for each photovoltaic storage system with the goal of minimizing the daily operation cost of the base station microgrid.The outer model aims to minimize the annual average comprehensive revenue of the 5G base station microgrid,while considering peak clipping and valley filling,to optimize the photovoltaic storage system capacity.The CPLEX solver and a genetic algorithm were used to solve the two-layer models.Considering the construction of the 5G base station in a certain area as an example,the results showed that the proposed model can not only reduce the cost of the 5G base station operators,but also reduce the peak load of the power grid and promote the local digestion of photovoltaic power.

Research and practice of designing hydro/ photovoltaic hybrid power system in microgrid

Small-hydro power station is often used in remote areas beside a river,but it doesn't match electricity demand so well,especially in dry seasons. A photovoltaic (PV) system with battery is a suitable option to complement the electricity gap. In this paper,a new structure of megawatt-class PV system integrating battery at DC-bus (DC: direct current) is proposed to be used in hydro/PV hybrid power system,and 4 main designing considerations and several key equipments are discussed. In 2011,a 2 MWp PV station with the proposed structure was built up in Yushu,China. From stability analysis,the station shows a strong stability under load cut-in/off and solar irradiance's fluctuation.

Optimal Design of Photovoltaic–Battery Systems Using Interval Type-2 Fuzzy Adaptive Genetic Algorithm

Many countries have been triggered to provide a new energy policy which promotes renewable energy applications because of public awareness to reduce the global warming and rising in fuel prices. Renewable energy sources such as solar energy are green and promising energy in the future for widespread use. Combining renewable energy sources with battery makes electricity supply more economical and reliable to meet all possible load level. This paper proposed a new hybrid method to optimize Photovoltaic (PV)-Battery systems. The proposed method was named Interval type-2 fuzzy adaptive genetic algorithm (IT2FAGA). Genetic algorithm (GA) is one of modern optimization techniques that has been successfully applied in various areas of power systems. To enhance the ability of GA to prevent trapping in? local optima and increase convergence in a global optima, the crossover probability (pcross) and the mutation probability (pmut), parameters in GA, are tuned using interval type-2 fuzzy logic (IT2FL). Objective function used in this paper was the annual cost of sytem (ACS) consisting of the annual capital cost (ACC), annual replacement cost (ARC), annual operation cost maintenance (AOM). The proposed method was also compared to fuzzy adaptive genetic algorithm (FGA) and standard genetic algorithm (SGA). Simulation results indicated that the

A digital twin model-based approach to cost optimization of residential community microgrids

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.

A Robust Single-Sensor MPPT Strategy for Shaded Photovoltaic-Battery System

A robust single-sensor global maximum power point tracking(MPPT)strategy based on modern optimization for photovoltaic systems considering shading conditions is proposed in this work.The proposed strategy is designed for battery charging applications and direct current(DC)microgrids.Under normal operation,the curve of photovoltaic(PV)output power versus PV voltage contains only a single peak point.This point can be simply captured using any traditional tracking method like perturb and observe.However,this situation is completely different during the shadowing effect where several peaks appear on the power voltage curve.Most of these peaks are local with only a single global.This condition leads to the incapability of traditional tracking approaches to extract the global peak power due to their inability to distinguish between the local and global peak points.They are trapped in the first peak point even when the point is local.Therefore,global tracking approaches based on modern optimization are highly required.A recent marine predators algorithm(MPA)has been used to solve the problem of tracking the global MPP under shadowing influence.Different shadowing scenarios are used to test and evaluate the performance of MPA based tracker.The obtained results are compared with particle swarm optimization(PSO)and ant lion optimizer(ALO).The results of the comparison con-firmed the effectiveness and robustness of the proposed global MPPT-MPA based tracker over PSO and ALO.

Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions.The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities.The scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action spaces.One of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy systems.The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep Q-learning.The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

Off-Grid Photovoltaic System Design for Haiti School Project

In this paper a Photovoltaic (PV) system was designed for the Port-Margot School Solar Project in Haiti. This off-grid system consists of PV panels, inverter, battery storage and other components such as fuses, dc/ac disconnects and transformers [1]. Sizing the PV to fit on the roof was determined. The battery storage and inverter were chosen to be installed in the school building. The expected energy production was compared with data file from NASA website. The KW PV system was chosen to be the most effective and demonstrating renewable technologies and reducing the electrical load of the school.

Dynamic Economic Dispatch for Microgrids Including Battery Energy Storage

Microgrids integrate distributed renewable energy resources, controllable loads and energy storage in a more economic and reliable fashion. Battery energy storage units are essential for microgrid operation, which make microgird become a strong coupling system in the time domain. Hence, the traditional methods of static dispatch are no longer suitable for microgrids. This paper proposes a dynamic economic dispatch method for microgrids. Considering microgrid as a discrete time system, the dynamic economic dispatch is to find the optimal control strategy for the system in finite time period. Based on this idea, the dynamic economic dispatch model for microgrids is established, and then the corresponding dynamic programming algorithm is designed. Finally, an example of microgrid is given, and the dynamic economic dispatch results are compared with that of the static dispatch. The comparison confirms the effectiveness of the proposed dynamic dispatch method.

Modeling and Experimental Verification of Electric Vehicles Off-Grid Photovoltaic Powered Charging Station

With the increasing development of EVs, the energy demand from theconventional utility grid increases in proportion. On the other hand, photovoltaic(PV) energy sources can overcome several problems when charging EVs from theutility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PV array, a lithium-ion battery representing the EV battery, and alead-acid battery representing the energy storage system (ESS). A bidirectionalDC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed controllers achieve maximum power extraction from the PV and regulate the DC-linkvoltage. It also controls the voltage and current levels of both the ESS and the EVduring the charging/discharging process. The study has been applied to two caseswith different power levels. Analysis, simulation, and implementation of the proposed system are presented. A 120 W laboratory prototype is carried out to verifythe system performance, experimentally. Design guides for higher power levelsare proposed to help in choosing the proper parameters of the converters. Boththe simulation and experimental results are matched and verify the highperformance of the proposed system.

Comparison between SiC- and Si-Based Inverters for Photovoltaic Power Generation Systems

100-W class power storage systems were developed, which comprised spherical Si solar cells, a maximum power point tracking charge control-ler, a lithium-ion battery, and one of two different types of direct current (DC)-alter- nating current (AC) converters. One inverter used SiC met-al-oxide-semicon-ductor field-effect transistors (MOSFETs) as switching devices while the other used Si MOSFETs. In these 100-W class inverters, the ON resistance was considered to have little influence on the efficiency. Nevertheless, the SiC-based inverter exhibited an approximately 3% higher DC-AC conversion efficiency than the Si-based inverter. Power loss analysis indicated that the higher efficiency resulted predominantly from lower switching and reverse recovery losses in the SiC MOSFETs compared with in the Si MOSFETs.

Impact of Photovoltaics

Photovoltaics(PV)can convert sunlight into electricity by making use of the photovoltaic effect.Solar panels consist of photovoltaic cells made of semiconductor materials(such as silicon)to utilise the photovoltaic effect and convert sunlight into direct current(DC)electricity.Nowadays,PV has become the cheapest electrical power source with low price bids and low panel prices.The competitiveness makes it a potential path to mitigate the global warming.In this paper,we investigate the relationship of PC array output with irradiance and temperature,the performance of PV array over 24 hours period,and the simulation of PV micro grid by MATLAB simulation.

Optimal Design of Grid-Connected Microgrid System for Cold Chain Logistics Centre in China

The growing interest in energy conservation has inspired companies to seek alternatives to highly polluting fuel electricity generation. This study designed an optimised solar wind power generation system to fulfil the energy requirement of a cold chain logistics centre. This study first conducted a thorough analysis of the clarity indicators and daily temperature positions of the cold chain logistics centre, determined the average daily electricity demand, and proposed an effective design scheme. The energy simulation software, RETScreen 8.0, was used to determine the scale of solar photovoltaic and wind power projects that meet the expected energy needs of the cold chain logistics centre. The results indicated that the estimated annual total energy demand was 833689.2 kWh. The annual power generation of 6 kW from solar photovoltaic projects and 150 kW from wind power projects was found to be enough to meet the expected electricity demand. Solar photovoltaic power generation and wind power generation account for 2.44% and 97.56%, respectively. The hybrid energy system achieved a 96.6% reduction in carbon emissions and provides a reasonable payback period of 6.1 years and an electricity generation of 904368.674 kWh per year. The feasibility of the project and the calculated period of investment return are very reasonable. Therefore, this hybrid renewable energy system provides reliable power by combining energy sources.

A review on capacity sizing and operation strategy of grid-connected photovoltaic battery systems

Due to the fluctuation and intermittency of distributed PV generation,battery energy storage is required with higher renewable installation towards carbon neutrality.Thus,the photovoltaic battery(PVB)system receives increasing attention.This study provides a critical review on PVB system design optimization,including system component sizing and strategy improvement studies,from mathematical modeling,evaluation system establish-ment to feasibility and optimization studies.Several PVB simulation software packages are compared and eval-uated,and acknowledged system models are presented.The evaluation indicators are summarized from various aspects with cases of various evaluation systems combining different indicators or using the Pareto front for multi-criteria system designing.The PVB system feasibility study is analyzed from system configuration varia-tion,critical technical and economic parameter analyses,rule-based operation strategies to future expectations like large-scale energy storage profitability,grid parity,and energy community trading platform.The targets,methods,tariffand time resolution influences,and PVB system capacity optimization design recommendations are critically discussed.The research directions for system operation development and future expectations are analyzed from system feasibility,flexibility to resilience.The co-planning of PVB system capacity and operation design optimization makes the problem complicated,leading to relatively short time resolution but more flexi-bility to system operation strategy.This study could provide guidance and references to distributed PVB system future design and optimization studies.

考虑保供电需求的光储微电网优化配置及电能质量评估

针对用户日益迫切的保供电需求以及光伏出力与负荷用电的随机性、波动性引发的电能质量的问题,建立了考虑保供电需求的光储微电网双层优化模型,并对规划配置的微电网电能质量进行评估。该模型以等效净负荷标准差和弃光率最小为上层优化目标,以微电网有功功率损耗最小为下层优化目标,以储能满足保供电负荷的保供电需求作为附加约束条件,采用遗传算法求解。然后设计源、网、荷三类指标,采用组合赋权法确定各指标权重,实现对微电网电能质量的综合评估。最后以改进的IEEE-33系统验证了所提模型的有效性。结果表明考虑保供电需求后能够提高微电网电能质量,并在一定范围内保供电负荷越大时越能提高微电网电能质量和用户满意度。

光伏硅弃料制备锂离子电池硅负极的性能

纯硅的导电性差,且纳米化、非晶化技术成本高。光伏行业用硅的产业化程度高、导电性好,弃料可在锂离子电池硅基负极实现再利用。以光伏硅弃料为原料,采用机械球磨法制备纳米硅和非晶/纳米硅材料。制备的材料均具有较高的可逆比容量和稳定的循环性能,以0.2 C在0.005~0.900 V循环100次,纳米硅剩余的比容量为2 123.32 mAh/g,非晶/纳米硅剩余的比容量为1 367.70 mAh/g。

基于SPEA2的风光柴储独立微电网多目标容量优化配置

在电力资源相对匮乏而自然风光资源丰富的孤岛等地区,电力供应的稳定性和成本效益一直是个亟待解决的问题。传统的独立微电网在容量配置时,大多依赖快速非支配排序遗传算法(NSGA-Ⅱ),该算法在处理真实负载的多目标优化问题时,局部搜索能力略显不足。为此,提出了利用改进强度Pareto进化算法(SPEA2)优化风光柴储独立微电网容量配置,以经济性成本、失负荷概率、碳排放作为优化目标,实现更加全面和高效的容量配置。通过导入某孤岛天气与负荷数据,生成风光柴储独立微电网的真实Pareto前沿,将SPEA2和基于指标选择的多目标搜索(IBEA)、NSGA-Ⅱ3种算法分析结果进行对比,相较于NSGA-Ⅱ,SPEA2的反世代距离评价IGD指标提升46.83%,空间评价方法Spacing指标提升60.28%,真实Pareto覆盖率CPF指标提升35.14%,该算法表现出更加出色的性能。最后根据容量优化的结果合理配置各部分参数,共同出力满足负荷需求,为孤岛等电力资源匮乏地区的能源管理提供了新的思路,也为多能源微电网的优化设计提供了有价值的参考。