Photovoltaic Energy Storage System Research Articles

Optimal Incorporation of Photovoltaic Energy and Battery Energy Storage Systems in Distribution Networks Considering Uncertainties of Demand and Generation

In this paper, the Archimedes optimization algorithm (AOA) is applied as a recent metaheuristic optimization algorithm to reduce energy losses and capture the size of incorporating a battery energy storage system (BESS) and photovoltaics (PV) within a distribution system. AOA is designed with revelation from Archimedes’ principle, an impressive physics law. AOA mimics the attitude of buoyant force applied upward on an object, partially or entirely dipped in liquid, which is relative to the weight of the dislodged liquid. Furthermore, the developed algorithm is evolved for sizing several PVs and BESSs considering the changing demand over time and the probability generation. The studied IEEE 69-bus distribution network system has different types of the load, such as residential, industrial, and commercial loads. The simulation results indicate the robustness of the proposed algorithm for computing the best size of multiple PVs and BESSs with a significant reduction in the power system losses. Additionally, the AOA algorithm has an efficient balancing between the exploration and exploitation phases to avoid the local solutions and go to the best global solutions, compared with other studied algorithms.

Comprehensive effectiveness assessment of energy storage incentive mechanisms for PV-ESS projects based on compound real options

Nowadays, the photovoltaic-energy storage system (PV-ESS) has not achieved large-scale development. The role of ESS incentive mechanisms has been emphasized for promoting the diffusion of PV-ESS technology. Therefore, to explore reasonable ESS incentive mechanisms in China, this paper develops a compound real options model by considering the flexibilities and uncertainties of the investment and operational stage. The impact of the carbon emission trading market, auxiliary service market, and different ESS incentive policies and their synergistic actions on PV-ESS investment have been explored by considering the social welfare of all related stakeholders. Moreover, a sensitivity analysis on the scale of expanding the investment and incentive intensity for ESS is conducted. The results show that the electricity price subsidy is more favorable for investing in the PV-ESS project. The investment will be brought forward 2 years compared to the situation without incentive policy. In addition, from the perspective of social welfare, the effects of the combinations of three ESS incentive policies are better than any single ESS policy, and the project value and social welfare increases by at least 3.46 and 7.99 million yuan, respectively. Illustrative examples can provide references for the government and investors in promoting the construction of PV-ESS projects.

Optimizing the Size of Autonomous Hybrid Microgrids with Regard to Load Shifting

The article proposes a method of multipurpose optimization of the size of an autonomous hybrid energy system consisting of photovoltaic, wind, diesel, and battery energy storage systems, and including a load-shifting system. The classical iterative Gauss–Seidel method was applied to optimize the size of a hybrid energy system in a remote settlement on Sakhalin Island. As a result of the optimization according to the minimum net present value criterion, several optimal configurations corresponding to different component combinations were obtained. Several optimal configurations were also found, subject to a payback period constraint of 5, 6, and 7 years. Optimizing the size of the hybrid power system with electric load shifting showed that the share of the load not covered by renewable energy sources decreases by 1.25% and 2.1%, depending on the parameters of the load shifting model. Net present cost and payback period also decreased, other technical and economic indicators improved; however, CO2 emissions increased due to the reduction in the energy storage system.

Renewable Energy Communities: Optimal sizing and distribution grid impact of photo-voltaics and battery storage

In this paper, an extensive study of Renewable Energy Communities and their potential impact on the electric distribution grid has been carried out. For that purpose, a Linear Programming optimization model sizing the energy community’s Photo-Voltaic and Battery Energy Storage System was developed. The linear programming model was soft coupled with power flow analysis to investigate the impact of different energy community configurations on distribution grids. Different distribution grids (city, suburban, village), different energy community configuration, different operating strategies and different battery placements were investigated. The results showed that when the battery is located at the beginning of the feeder, then the energy community does not impact the observed minimum and maximum voltage. Moreover, it was found that depending on the energy community’s operating strategy the low-voltage grid loading can be reduced by up to 58 %. The energy community’s sizing showed that optimal capacities of photo-voltaics and communal batteries were up to three times larger for the case of city grid, following the operating strategy of maximizing the energy community’s own economic benefit than in other operating strategies and grid types.

Research on Multi-Objective Optimization of Household Photovoltaic Energy Storage and Grid System

With the integration of large-scale photovoltaic systems, many uncertainties have been brought to the grid. In order to reduce the impact of the photovoltaic system on the grid, a multi-objective optimal configuration strategy for the energy storage system to discharge electricity into the grid is proposed. On the basis of the time-of-use electricity price, the total load variance and the user’s profits are taken as two objective functions. Constraints such as the maximum discharge power limit of the energy storage system are taken into account and a multi-objective optimal scheduling model for the centralized discharge of the energy storage system is established. A multi-objective genetic algorithm is used to solve the optimization model. The calculation results of MATLAB verify the feasibility and effectiveness of the strategy and the influence of different energy storage system capacities on the optimization results are also analyzed.

System modeling of micro‐grid with hybrid energy sources for optimal energy management—A hybrid elephant herding optimization algorithm‐adaptive neuro fuzzy inference system approach

AbstractThis manuscript proposes a hybrid method for system modeling and optimal allocation of low cost micro‐grid. The proposed hybrid method is the joint execution of the enhanced elephant herding optimization algorithm (EHOA) and adaptive neuro fuzzy inference system (ANFIS) named as EHO‐ANFIS. By using MG inputs, such as solar photovoltaic, wind turbine (WT), micro turbine (MT), fuel cell (FC), and battery energy storage system. EHO optimizes micro‐grid configuration in minimal fuel costs based on needed load requirement. Here, learning phase of ANFIS is utilized for predicting the load requirement. EHOA reduces operation and maintenance costs, emission cost on the basis of the predicted load requirement. The proposed method is executed in MATLAB/Simulink site and the robustness of the proposed method is compared with different existing methods. In the proposed method, the maximal generated power of photovoltaic represents 6 kW, wind turbine indicates 7.8 kW, micro turbine denotes 11.8 kW, FC implies 6.8 kW, and battery refers 3 kW. By utilizing genetic algorithm, the generated power of photovoltaic signifies 7 kW, WT implicates 6 kW, MT implicates 4 kW, FC refers 7 kW, and battery implies 14 kW. The proposed method has minimal cost effective depending on its load demand. The computational time of the proposed technique under 100, 250, 500, and 1000 trails is 5600, 14 000, 28 000, and 56 000 s.

A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions

To satisfy the grid-connected voltage level, both photovoltaic modules and energy storage modules are connected in series. However, the multiple photovoltaic modules often fall into local maximum power point under partial shading conditions during practical operation, and the multiple energy storage modules may suffer from a reduction in the effective capacity caused by characteristic differences among modules. To solve this problem, a novel cascaded modular photovoltaic-energy storage system is proposed in this paper. In the proposed topology, the energy storage modules achieve maximum power point tracking of the corresponding distributed photovoltaic module, and the proposed energy optimization strategy based on particle swarm optimization can ensure the efficient constant active power transmission from a photovoltaic energy storage (PV/ES) system to the grid in a certain time period under capacity constraints. Compared with conventional photovoltaic systems, the proposed scheme can avoid hot spots or the hot strings phenomena for PV modules and the large current and voltage stresses for DC/DC converters. Furthermore, the proposed energy optimization strategy for the coordination of all ES modules can realize the independent MPPT of each PV module and the constant active power between the PV/ES system and the grid under inconsistency of the light intensity under partial shading conditions. A hardware-in-loop photovoltaic-energy platform is established to verify the feasibility and effectiveness of the proposed topology and control strategy, and the proposed system achieves efficiency of about 97% under partial shading conditions, thus providing an effective and practical solution for power generation system.

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the ε-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in the form of a HIS finds optimal values for economic and environmental conditions so that the difference between pollution and cost values from its minimum values at the compromise point is roughly 22%. For 17% uncertainty parameters prediction errors, the scheme obtains a robust structure for the IHS.

Multiport Converter based EV Charging Station with PV and Battery

: As a ecofriendly electrical vehicle, is vehicles that are used electric motor or traction motor. Are receiving widespread attention around the world due to their improved performance and zero carbon emission . The electric vehicle depend on photovoltaic and battery energy storage system . Electric vehicles include not limited road and railways. It consist of many electric appliances for use in domestic and industrial purposes that is electric car ,electric bike ,electric truck ,electric trolley bus , electric air craft ,electric space craft.The main Moto of this paper is a modelling of proposed system smart charging for electrical vehicle insuring minimum stress on power grid . The large scale development of electrical vehicle we need electric charging station for example fast charging station and super-fast charging station . During a peak demand load , large load on charging station due to the voltage sag , line fault and stress on power grid . At this all problem avoid by multiport converter based EV charging station with PV and BES by using analysis of MATLAB simulation. Result and conclusion of this paper to reduce losses improving efficiency of solar energy , no pollution (reduce) fast charging as possible as without any disturbance.

Optimal Sizing of Rooftop PV and Battery Storage for Grid-Connected Houses Considering Flat and Time-of-Use Electricity Rates

This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity.

Bidirectional Power Flow Control and Hybrid Charging Strategies for Three-Phase PV Power and Energy Storage Systems

The objective of this article is to propose a photovoltaic (PV) power and energy storage system with bidirectional power flow control and hybrid charging strategies. In order to optimize the battery charging performance, five charging strategies, including the constant-current charging, the pulse-ripple-current charging, the sinusoidal-ripple-current charging, the bidirectional pulse-ripple-current, and the bidirectional sinusoidal-ripple-current charging, are adopted. Traditionally, in order to realize these charging strategies, the PV charger should abandon the maximum power point tracking function to maintain the power flow balance. As a result, the output power of the PV array will be decreased. Therefore, bidirectional power flow control strategies are proposed to achieve the maximum PV power utilization as well as to realize the hybrid charging methods. In addition, with the proposed strategies, the bidirectional charging/discharging capability of the battery is able to achieve the maximum PV power utilization. All the proposed strategies can be realized by the digital signal processor without adding any additional circuit, component, and communication mechanism. Theoretical analysis and control principles will also be revealed. Finally, a 5-kW prototype circuit with both simulation and experimental results demonstrate the performance and feasibility of the proposed strategies.

Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.

Impact of fault impedance and duration on transient response of hybrid AC/DC microgrid

This paper proposes a mechanism for time-frequency analysis of post-fault transient response in hybrid AC/DC microgrids. The proposed mechanism is based on the continuous wavelet transform of the electromagnetic transient (EMT) simulation data in grid-connected and islanded modes of operation. The proposed mechanism is used to evaluate the impact of fault parameters on the post-fault voltage recovery and to estimate the critical clearance time of the faults. Moreover, a simulation benchmark is developed that involves different types of distributed energy resources (DERs) including solar photovoltaic (PV), wind, and battery energy storage systems. The developed microgrid benchmark is implemented in EMTP software and extensive simulation results are analyzed to obtain insights into the voltage recovery and transient response of hybrid AC/DC microgrid subject to faults.

Hybrid improved Sparrow Search Algorithm and sequential quadratic programming for solving the cost minimization of a hybrid photovoltaic, diesel generator, and battery energy storage system

ABSTRACT In this paper, a hybrid photovoltaic/battery/diesel generator power generation system is designed and optimized to power a network of a remote village in China. The purpose of the system designed here is to lessen the fuel costs of the system dependent on the load demand and some constraints. Therefore, the idea comprises an optimization problem that is solved here based on a new optimization policy by an Improved design of Sparrow Search Algorithm (imSSA) and the Sequential Quadratic Programming (SQP). The results of the proposed method are analyzed in different seasons and also on weekdays and weekends to show their effect on the operational cost of the PV/Diesel BESS system. Final results indicate that in both winter and summer, the weekday costs are lesser than weekend fuel costs. Furthermore, the summer fuel cost is lesser than the winter fuel costs which is because of lesser demand in summer and the higher levels of summer radiation imply less usage of auxiliary origins. The results show that at the weekends cost more than the weekday. For instance, for the proposed mode, the winter weekends and the weekdays’ costs are US$12.1 and US$10.2, respectively, or the summer weekends and the weekdays’ costs are US$7.3 andUS$5.7, respectively.

Energy management strategy of photovoltaic hybrid energy storage system based on optimal power distribution

Energy management strategy of photovoltaic hybrid energy storage system based on optimal power distribution

Robust Partial Feedback Linearized Controller Design for Standalone Hybrid PV-BES System

This paper presents a mixed-sensitivity-based robust H∞ loop-shaping partial feedback linearized control scheme to enhance the transient stability of a battery energy storage-associated standalone solar photovoltaic system. The proposed control scheme has been provided independent operating points for a generalized nonlinear dynamical model of DC microgrids connected standalone hybrid solar photovoltaic and battery energy storage system. A parametric uncertainty model is developed for the generalized dynamical model, and the noise disengaging merit of the proposed control technique has been investigated. The designed controller’s performance has been demonstrated under four different scenarios, and it is compared with the conventional PI controller for partial feedback linearized control law.

Function and practice of photovoltaic building energy storage system with low-voltage DC power supply

Function and practice of photovoltaic building energy storage system with low-voltage DC power supply

Decision Support Framework for Resilience-Oriented Cost-Effective Distributed Generation Expansion in Power Systems

Resilience of electricity grids to rare but severely disruptive events, such as natural disasters, has emerged in recent years as an important aspect in power system planning. Development of resilience-oriented techniques are needed due to the limitations of reliability-oriented methods in addressing large unexpected outages. This article presents a novel decision analysis approach to enhance the resilience of a power distribution system by leveraging the distributed nature of solar photovoltaic (PV) and battery energy storage system (BESS) resources. The proposed decision-making framework uses an analytic hierarchy process to evaluate different possible allocations of PV and BESS resources to load buses for multiple contingencies based on resilience enhancement and cost. Each allocation plan provides the size of PV arrays and BESS units to be installed at each bus within the distribution system. A deterministic formulation ranks line outage scenarios based on unserved load. The tradeoff between cost and resilience is modeled via cost-effectiveness analysis (CEA) that indicates the preferability of each allocation plan. The main contribution of this article is the development of an adaptable and computationally feasible decision support framework to determine cost-effective configurations of PV arrays and BESSs for mitigating the effects of power line outage contingencies. To evaluate the proposed framework, a case study is carried out on the IEEE 33-bus radial distribution system. The results show that the proposed method can offer effective guidance to the planner for making informed investment decisions and achieving cost-effective resilience enhancement.

Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e., it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.

Modeling and real time digital simulation of microgrids for campuses Malta and Jordan based on multiple distributed energy resources

<p>This paper presents the modeling and real-time digital simulation of two microgrids: the malta college of arts, science and technology (MCAST) and the german jordan university (GJU). The aim is to provide an overview of future microgrid situation and capabilities with the benefits of integrating renewable energy sources (RES), such as photovoltaic panels, diesel generators and energy storage systems for projects on both campuses. The significance of this work starts with the fact that real measurements were used from the two pilots, obtained by measuring the real physical systems. These measures were used to plan different solutions regarding RES and energy storage system (ESS) topologies and sizes. Also, the demand curves for the real microgrids of MCAST and GJU have been parameterized, which may serve as a test bed for other studies in this area. Based on actual data collected from the two pilots, a real-time digital simulation is performed using an RT-LAB platform. The results obtained by this tool allow the microgrid manager to have a very accurate vision of the facility operation, in terms of power flow and default responses. Several scenarios are studied, extracting valuable insight for implementing both projects in the future. Eventually, the proposed models would be a blueprint for training and research purposes in the microgrid field.</p>