Utilization Of Photovoltaic Energy Research Articles

Dual-objective optimization of solar driven alkaline electrolyzer system for on-site hydrogen production and storage: Current and future scenarios

Achieving cost competitiveness of renewable hydrogen could accelerate the transition to the deeply decarbonized energy system. In this article, we develop and apply a dual-objective optimization model to explore the photovoltaic (PV) hydrogen production pathway and costs development from the present through 2050. The model is applied for optimal capacity allocation of the megawatt-scale off-grid PV-Hydrogen system to achieve maximum production at the minimal levelized cost of hydrogen (LCOH). Methodology includes a smoothing control strategy. Simulation is performed utilizing measured meteorological data for one year with hourly resolution and considering electrolyzer's load flexibility constraint. It has been found that the smoothing control strategy is indispensable for maximizing PV energy utilization, enhancing the electrolyzer's capacity factor and reducing the power curtailments. The analysis shows that the off-grid solar hydrogen in Algeria lacks economic competitiveness currently. Components CAPEX reduction turns out to be the fundamental condition towards the future LCOH decrease. LCOH could decline from 4.2 $/kg in 2025 to 2.24 $/kg in 2050 under central assumptions and to roughly 1.4 $/kg under optimistic assumptions. Alkaline electrolysis step cost could reduce by 0.28 $/kg every decade. Hydrogen storage autonomy could rise the LCOH by 7.1 c$ per day of autonomy in 2050.

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Study on the synergistic regulation strategy of load range and electrolysis efficiency of 250 kW alkaline electrolysis system under high-dynamic operation conditions

Alkaline water electrolysis (AWE) has the highest technological maturity among all the water electrolysis technologies for hydrogen production, however, reducing the minimum load boundary and improving the electrolysis efficiency are the technical challenges of the AWE system that still exist and urgently require optimization. The minimum load is primarily limited by the hydrogen to oxygen (HTO) from cross-diaphragm transfer and lye mixing, with HTO above 2.0% being a significant safety risk. Reducing the lye flow rate and pressure are effective while two of the few ways by regulating the operating parameters to improve the HTO thus extend the minimum load boundary, but will worsen electrolysis efficiency. Therefore, this study proposes a synergistic regulation strategy of pressure and lye flow rate: maximizing pressure and lye flow rate during high load period to ensure high electrolysis efficiency; adjusting lye flow rate and pressure during medium load period to ensure HTO≤2.0% and maximize the electrolysis efficiency; and reducing lye flow rate and pressure to a low level during the low load period to broaden the minimum load so as to improve overall efficiency of AWE system when loading with fluctuant green electric. This work elaborates the HTO routes, influencing factors and parameter optimization mechanism by building a system-level steady-state and dynamic gas purity model. The optimal combination curve of pressure and lye flow rate is obtained and its control effect on performance parameters, in terms of minimum load, system energy consumption, energy utilization, electrolysis efficiency and so on, is compared and verified in high dynamic wind and photovoltaic (PV) power scenarios. Finally, the optimal wind & PV power ratios are explored based on the optimal operation curve, which will provide a reference for the future large-scale development of hydrogen production scenarios direct-coupled with wind and PV power. The minimum load is extended from 42.0% in the lye flow rate alone control to 21.2% in the pressure alone control and finally to 15.6% in the lye flow rate and pressure synergistic control method. In the absence of electrical replenishment, wind and PV energy utilization efficiency can reach up to 98.3% and 95.6%, respectively.

Hierarchical Energy Management of Networked Flexible Traction Substations for Efficient RBE and PV Energy Utilization Within ERs

The particular split-phase traction power system of AC electrified railways (ERs) restricts its regenerative braking energy (RBE) utilization and photovoltaic (PV) penetration. Networked flexible traction substation (FTSS) operation is a promising paradigm to address this restriction. Therefore, this paper proposes a networked FTSS system architecture that ties adjacent FTSSs via unified power flow controllers (UPFC) at sectioning posts (SPs), which matches the technical characteristics of split-phase ERs obtaining the lower capacity requirement. Correspondingly, a real-time hierarchical energy management strategy is designed. The networked FTSS management level optimizes the energy cooperation among FTSSs, i.e., the exchange power at SPs. It aims at maximizing the global RBE and PV energy utilization and shaving the maximum demand of each FTSS. At the individual FTSS management level, each FTSS autonomously regulates the power outputs of the local back-to-back converter and energy storage system to maximize the local RBE and PV energy utilization and improve the local power quality issues. These two energy management levels operate independently with different time scales, making the networked FTSS system have good extensibility and reliability. Finally, the performance of the proposed scheme is verified via hardware-in-the-loop tests.

Optimal scheduling of residential houses with optimal photovoltaic energy utilization strategy using improved multi-objective equilibrium optimizer algorithm

In the background of strongly practicing the “double carbon” strategy, the use of solar photovoltaic (PV) energy in residential houses is an effective way to achieve energy saving and emission reduction. In this paper, a multi-objective optimal scheduling model for residential houses is established, and it takes electricity cost, demand response (DR) curtailment value and carbon emission as the optimization objectives. Then, a novel operation strategy, which is called optimal PV energy utilization strategy, is proposed to further reduce electricity cost and carbon emission, by properly managing the power flow. For the multi-objective optimal scheduling problem with multi-decision variables and nonlinearity, an improved multi-objective equilibrium optimizer (IMOEO) algorithm is proposed to obtain the Pareto front solutions. The algorithm uses constrained non-dominant mechanism to deal with the constraint problems of the model, as well as the hybrid opposite learning strategy and the spiral operator are used to improve the overall performance of the algorithm. Finally, the optimal compromise solution is obtained by using the technique for order preference by similarity to ideal solution (TOPSIS). The experimental results indicate that IMOEO algorithm has better performance in terms of convergence and diversity. What's more, compared with the original strategy, the optimal compromise solution obtained by the proposed strategy reduces 28.76%, 9.78% and 15.36% for these optimization objectives, respectively. Therefore, the proposed method is beneficial to achieve low-carbon electricity consumption in residential houses.

Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households

The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on the power grids and helping manage renewable energy resources. This paper evaluates the effect of dynamic electricity prices and home photovoltaic (PV) system incentives on users’ EVs charging behavior and potential impacts on grid load and household economy. This has been done by establishing and assessing three different optimized charging configurations and comparing them to an uncontrolled charging strategy. In this study, the charging incentives are applied to a representative sample of 100 households with EVs and PV systems in a metropolitan area. The results show that an optimized charging strategy based on the dynamic electricity tariff can reduce charging costs by 18.5%, while a PV-based optimized strategy can reduce the costs by 33.7%. Moreover, the PV-integrated optimization strategies significantly increase the utilization of PV energy by almost 46% on average, compared to uncontrolled charging. In addition, the simulations of this research have depicted the capability of using home PV systems’ incentives to smoothen the charging profiles and hence significantly reduce the maximum grid load. However, the electricity price optimization strategy increases the aggregated charging peaks, which can only be slightly reduced by peak shaving. Therefore, an identical price signal for all households might be critical. Further analyses have shown that direct charging occurs simultaneously with household electricity assigned to a specific low-voltage grid while PV and price incentive charging configurations shift the charging peaks away from household load peaks.

Power Limit Control Strategy for Household Photovoltaic and Energy Storage Inverter

The increased installation capacity of grid-connected household photovoltaic (PV) systems has been witnessed worldwide, and the power grid is facing the challenges of overvoltage during peak power generation and limited frequency regulation performance. With the dual purpose of enhancing the power grid safety and improving the PV utilization rate, the maximum feed-in active power can be regulated by modifying the maximum power point tracking (MPPT) algorithm and battery energy storage (BES) accessibility as control instructions. However, the existing methods not only waste installed PV capacity, but it becomes no longer accessible when the state of charge (SOC) of the BES approaches its upper limit. In response to the above problem, this paper proposes a power limit control strategy to coordinate the MPPT algorithm and the BES accessibility. The proposed strategy directly controls the inverter output current according to the power limit instructions from the electric operation control centers, leading to a bus voltage difference. The difference serves as a control signal for BES and PV. Under a power-limiting scenario, priority is given to power regulation through energy storage to absorb the limited active power. When the SOC of the BES reaches the upper limit of charging, modification of the PV MPPT algorithm facilitates the inverter output power to meet the power limit requirements. To further verify the effectiveness of the proposed power limit control strategy, both simulation and experimental studies are conducted, which consistently indicated a synchronized inverter current with grid voltage and a rapid power response of the power-limiting instruction within 0.2 s. The power limit control strategy not only improves the PV energy utilization but also supports the safe and reliable operation of the power gird in the context of soaring renewable energy penetration.

PV Inverter Reliability-Constrained Volt/Var Control of Distribution Networks

The increasing penetration of photovoltaic (PV) systems promotes utilization of PV inverters for volt/var control (VVC) in distribution networks. However, PV inverters are vulnerable and their reliability is one of the most critical concerns for sustainable PV energy utilization. To enhance PV inverter reliability, this paper proposes a PV inverter reliability-constrained VVC method of distribution networks under uncertainties. Firstly, a full reliability analysis procedure for PV inverters is developed to estimate the lifetimes of core devices inside inverters. Secondly, considering the impacts of VVC on PV inverter reliability, reliability constraints are proposed based on regulation of inverter apparent power and application of a PV curtailment scheme. Thirdly, a multi-objective PV inverter reliability-constrained VVC optimization model is proposed, which aims to minimize network power loss and PV curtailed power, simultaneously. Accordingly, the nonconvex model is relaxed by a second-order cone programming method, and the uncertainties such as PV power generation and loads are addressed by a scenario-based stochastic optimization method. The proposed VVC method is tested on the 33-bus and 69-bus distribution networks, and the simulation results verify high efficiency of the proposed method in energy loss reduction and inverter reliability enhancement.

Enhancing Utilization of PV Energy in Building Microgrids via Autonomous Demand Response

This article investigates the autonomous demand response (ADR) in a building microgrid incorporating photovoltaic (PV) generation and plug-in electric vehicles. Two operation models are introduced in this article to enhance the self-utilization of PV power: 1) mixed integer programming (MIP)-based optimization model; and 2) the game theoretic model. To avoid the disadvantages of MIP-based centralized optimization in a decentralized approach, a non-cooperative ADR game framework is formulated based on the proposed virtual cost mechanism for each player to help in selecting the optimal consumption strategy coordinately. The existence of the unique Nash equilibrium which coincides with the optimal solution of the MIP-based operation model is proved. In addition, an iterative algorithm is developed to determine the equilibrium solution for the ADR game. Simulation results verify that the non-cooperative game-based ADR program is effective in improving the utilization of PV energy and benefits to microgrid systems.

Solar Irradiance Forecasting in Remote Microgrids Using Markov Switching Model

Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduces the benefits of PV integration. These challenges can be handled by introducing reserve. However, this leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. This paper presents a new solar irradiance forecasting method for remote microgrids based on the Markov switching model. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers past solar irradiance data, clear sky irradiance, and Fourier basis expansions to create linear models for three regimes or states: high, medium, and low energy regimes for days corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, USA, discussed in this paper, resulted in an average mean absolute percentage error of 31.8% for five years, from 2001 to 2005, with higher errors during summer months than during winter months.

High-Voltage DC-DC Converter Topology for PV Energy Utilization—Investigation and Implementation

This paper exploited the utilization of photovoltaic (PV) energy system with high-voltage (HV) output DC-DC converter. Classical boost converters are used for both renewable energy integration and HV applications, but limited by reducing output/efficiency in performance. Moreover, as parasitic elements suppress the power transfer ratio, converter needs to maximize the PV energy utilization. This investigation study focused to include additional parasitic elements (voltage-lift technique) to a standard DC-DC buck converter and to overcome all the above drawbacks to maximize the PV power generation. The proposed power circuitry substantially improves the output power gain transfer ratio and a prototype hardware module is implemented using industrial standard DSP TMS 320F2812. Numerical simulation development followed by an experimental prototype implementation is carried out in this investigation. A set of numerical and experimental results is provided in this paper, which show close conformity with the theoretical background.

A Charging Strategy for PV-Based Battery Switch Stations Considering Service Availability and Self-Consumption of PV Energy

The photovoltaic (PV)-based battery switch station (BSS) is one of typical integration systems to implement a solar-to-vehicle system. The charging strategy is important for the operation of the PV-based BSS. Generally, instant charging strategy for swapped electric vehicle (EV) batteries can keep the availability of battery-swapping service at a high level. However, it is always accompanied with the possibility of bringing a negative effect on the utilization of PV energy. The contribution of this paper is mainly on a novel charging strategy for the PV-based BSS considering the service availability and self-consumption of the PV energy. First, considering the features of the PV-based BSS, evaluation indexes for the operation performance are defined, including the availability of battery-swapping service, self-consumption of the PV energy, and operation profit. Second, the charging strategy is proposed, including a battery-swapping service model and a power distribution model. In order to guarantee the service availability, the battery-swapping service model is used to decide the lower limit of charging power based on short-term forecasting results of EV requirements. The power distribution model is obliged to dispatch the charging power supplied by the PV system and power grid. Finally, in the case study, the operation of the BSS is simulated with the instant charging strategy and the proposed strategy under different scenarios. From the analysis of results, the proposed strategy can effectively improve the self-consumption of PV energy with the premise of guaranteeing the availability of the battery-swapping service.

Editorial Special Issue on Power Electronics in Photovoltaic Applications, 2013

This Special Issue aims to address current challenges in the design and operation of Solar or photovoltaic (PV) energy systems, to report on novel ideas for enabling the wider utilization of PV energy and to explore optimal energy management control strategies for the PV-based Power Electronic System. The readers of this Special Issue will finally decide how well the previously stated goals are met. The accepted papers are not the only media by means of which the new ideas will be and have been promoted within the power electronics field. The most important media are actually the reviewers? comments stating exactly, why the paper is rejected or the major/minor revision is needed, because they initiate the necessary mental processes in the authors and also in the reviewers leading finally sooner or later to the more comprehensive understanding of the topic area. This Special Issue received 151 original submissions of which 36 were accepted for publication at the time ofwriting this editorial. Acouple of submitted papers are still in the process and will be published later according to the normal publishing practices. Majority of the accepted papers (51%) are related to different design and control aspects of grid-connected single- and threephase inverters. The second largest category of papers (22%) is related to the maximum-power-point-tracking (MPPT) issues. The rest of the accepted papers are related to the implementation of distributed MPPT schemes (8%), the reliability issues in PV systems and converters (8%), the properties of PV generator, and other timely issues in the PV processing (11%); Ninety percent of the 151 papers were submitted during the last two weeks the submission opportunity was open.