Load-following Strategy Research Articles

Comprehensive evaluation of part-load exergy performance of a supercritical carbon dioxide recompression cycle controlled by various strategies

This study seeks to provide guidance for the efficient operation of a supercritical carbon dioxide recompression cycle (sCO2-RC) by conducting a comprehensive assessment of the exergy performance under various load-following strategies. As the load decreases, we analyze the operational characteristics of the compressor and identify the components with higher irreversible proportions for the single strategy-controlled system. When the load diminishes to 10%, specific components exhibit significant irreversibility, excluding the compressor, as follows: 14.48% for the turbine under inventory control, 12.64% for the bypass mixer under turbine bypass control, and 22.10% for the bypass mixer under intermediate heat exchanger (IHX) bypass control. Owing to the compressor's near-surge operation, the turbine throttle control only minimally reduces the system load to 30%, with the throttle valve incurring the highest loss proportion of 26.33% at that point. To both ensure safe compressor operation and enhance system efficiency, auxiliary strategies such as inventory and turbine bypass are recommended to implement hybrid control. The assisted inventory improves the exergy efficiency of the system under turbine bypass control and IHX bypass control by 4.14% and 7.16% at 10% load, respectively. However, the adoption of auxiliary strategies may introduce additional losses while improving the performance of existing components. Although the assisted turbine bypass widens the adjustable load range of the turbine throttle-controlled system, it decreases the system's exergy efficiency by 3.65% at 30% load due to the introduction of a bypass mixer with higher exergy loss.

Evaluation of stand-alone hybrid renewable energy system with excess electricity minimizer predictive dispatch strategy

The prioritization of economic and reliability objectives in the optimization of stand-alone microgrids and off-grid hybrid renewable energy systems (HRES) has led to optimum systems with high excess electricity and low self-consumption indexes. Managing surplus power after charging the storage bank without imposing a negative impact on energy cost (COE) and environmental factors is a challenge for off-grid applications. In recent years the development of predictive dispatch strategies (PD), improves the energy efficiency in HRES compared to conventional dispatches such as load following (LF). However, the mismatch between renewable power production and demand profiles prohibits high excess electricity reduction. In this study, a novel dispatch strategy, entitled modified predictive dispatch (MPD), is developed by integrating a proactive approach to charging/discharging the battery bank and demand-side management (DSM) based on the upcoming short-term excess electricity profile. The results showed that with the participation of an average of less than 20 % deferrable components and the possibility of load shifting for less than 3 h, the excess electricity can be reduced to about 7.5 %, compared to 41 % and 18.5 % for LF and PD strategies, respectively. Furthermore, the self-utilization index of surplus power improved from less than 25 % to about 90 %. These improvements were achieved through the MPD strategy, which reduced COE by more than 10 %, reaching about 0.12 $/kWh without any significant effect on emission factors. The sensitivity analysis indicated that achieving an excess electricity self-utilization index of over 80 % requires a load-shifting possibility of more than 2 h and a deferrable load participation of about 10 % in the DSM approach. Therefore, results approved the effectiveness of the proposed dispatch strategy in high renewable penetration systems, even with low levels of participation by power consumers.

Simulation of the NuScale SMR and Investigation of the Effect of Load-Following on Component Lifetimes

The NuScale small modular reactor (SMR) has been modeled using the Virtual Environment for Reactor Applications multiphysics environment and the results compared with the publicly reported data in the Design Certification Application. The results show an excellent agreement for the compared axial and radial power distributions, temperature coefficients of reactivity, boron and control rod worths, and fast neutron flux. This NuScale model is then used to investigate the effect of different operational modes on reactor components to determine how the flexible load-following operation may affect control rod and reactor pressure vessel (RPV) lifetimes. The control rod degradation is confirmed to primarily affect the silver-indium-cadmium rod tip. The degradation rate is observed to follow a nonlinear function of core power level where the increase in degradation decreases with insertion depth. For the variation in core power levels expected with current load-following schemes, the total control rod degradation is found to be mild, at 5% to 10% of usable life per cycle for a reactor operating at <80% power. Nonetheless, this enables load-following strategies to be confirmed and/or modified to ensure that control rods do not need to be replaced during the 60+ year life of the reactor. The RPV degradation was found to be almost directly proportional to the core power level and was not overly sensitive to flux shape perturbations. Future work is planned using these damage functions to optimize operation over multiple NuScale SMR units and to develop strategies for prognostics and health management.

Design of cost-based sizing and energy management framework for standalone microgrid using reinforcement learning

The standalone photovoltaic-battery energy storage (PV-BES) microgrid has gained substantial interest recently due to its ability to provide uninterrupted power to consumers in remote areas. In such microgrids, components must be precisely sized and energy must be supplied most cost-effectively at all times. This paper presents a cost-based framework for determining the optimal size and energy management of standalone microgrids using reinforcement learning. Fundamental to this framework is two essential phases; the first is finding the best size of PV-BES using an analytical and economic sizing (AES) model based on minimum levelized cost of energy (LCOE). The AES phase is then followed by optimizing the energy management strategy (EMS) of the microgrid using reinforcement learning to provide optimum cost savings. The novelty in this work can be outlined as optimizing both the size and EMS of a standalone PV-BES microgrid using the AES model and Q-learning in an integrated framework. This can lead to improved performance demonstrated in reducing the LCOE, decreasing diesel generator working hours, and enhancing PV utilization and system efficiency. The results show an advantageous reduction in total cost while meeting load requirements. Additionally, the proposed framework is evaluated using several metrics to measure the impact of employing Q-learning against the AES-finite automata model. For instance, a decrease of 22% in diesel generator working hours and an increase of 6% in PV utilization while a reduction of 11% in the LCOE is accomplished. On the other hand, the proposed framework is examined against two rule-based EMSs, load following strategy (LFS) and cycle charging strategy (CCS), and outperforms these two EMSs in terms of LCOE, PV utilization, and system efficiency.

Optimal Operation of an Integrated Hybrid Renewable Energy System with Demand-Side Management in a Rural Context

A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB© environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system’s performance.

Assortment of Dispatch Strategies with the Optimization of an Islanded Hybrid Microgrid

In this work, the optimization of an off-grid micro-hybrid system is evaluated. This is conducted with the estimation of the proper sizing of each element and the steady-state voltage, frequency, and power responses of the microgrid. Kangaroo Island in South Australia is considered to be the test case location and the grid incorporates solar PV (photo-voltaic), diesel generator, battery storage, and wind turbine. Optimal sizing of the studied microgrid is carried out for four various power dispatch techniques: (i) cycle charging (CC), (ii) generator order (GO), (iii) load following (LF), and (iv) combined dispatch (CD). The proposed off-grid micro-hybrid is optimized for three performance indices; minimal Levelized Cost of Energy (LCOE), CO2 emission, and Net Present Cost (NPC). Using iHOGA (improved hybrid optimization by genetic algorithm), microgrid optimization software, all the above-mentioned dispatch strategies have been implemented and following this, MATLAB/Simulink platform has been used for the steady-state studies. The results show that the LF strategy is the utmost optimum dispatch technique in terms of the studied performance indices i.e. considering the optimal size and voltage and frequency responses. The results obtained from these studies provide a pathway for the estimation of the resource-generation-load combination for the islanded off-grid microgrid for its optimal operation with the various dispatch strategies.

Efficient Flatness Based Energy Management Strategy for Hybrid Supercapacitor/Lithium-ion Battery Power System

This article offers a flatness theory-based energy management strategy (FEMS) for a hybrid power system consisting of a supercapacitor (SC) and lithium-ion battery. The proposed FEMS intends to allocate the power reference for the DC/DC converters of both the battery and SC while attaining higher efficiency and stable DC bus voltage. First, the entire system model is analyzed theoretically under the differential flatness approach to reduce the model order as a flat system. Second, the proposed FEMS is validated under different load conditions using MATLAB/Simulink. Thus, this FEMS provides high-quality energy to the load and reduces the fluctuations in the bus voltage. Moreover, the performance of the FEMS is compared with the load following (LF) strategy. The obtained results show that the proposed FEMS meet the real load power under fast variations with good power quality compared to the classical LF strategy, where the maximum overshoot of the bus voltage is 5%.

A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids

Renewable-powered “undergrid mini-grids” (UMGs) are instrumental for electrification in developing countries. An UMG can be installed under a—possibly unreliable— main grid to improve the local reliability or the main grid may “arrive” and connect to a previously isolated mini-grid. Minimising costs is key to reducing risks associated with UMG development. This article presents a novel market-logic strategy for the optimal operation of UMGs that can incorporate multiple types of controllable loads, customer smart curtailment based on reliability requirements, storage management, and exports to and imports from a main grid, which is subject to failure. The formulation results in a mixed-integer linear programming model (MILP) and assumes accurate predictions of the following uncertain parameters: grid spot prices, outages of the main grid, solar availability and demand profiles. An AC hybrid solar-battery-diesel UMG configuration from Nigeria is used as a case example, and numerical simulations are presented. The load-following (LF) and cycle-charging (CC) strategies are compared with our predictive strategy and HOMER Pro’s Predictive dispatch. Results prove the generality and adequacy of the market-logic dispatch model and help assess the relevance of outages of the main grid and of spot prices above the other uncertain input factors. Comparison results show that the proposed market-logic operation approach performs better in terms of cost minimisation, higher renewable fraction and lower diesel use with respect to the conventional LF and CC operating strategies.

Greedy energy management strategy and sizing method for a stand-alone microgrid with hydrogen storage

This paper presents a greedy energy management strategy based on model predictive control (MPC) for a stand-alone microgrid powered by photovoltaic (PV) arrays and equipped with batteries and a power-to-hydrogen-to-power (P2H2P) system. The proposed strategy consists of a day-ahead plan and an intra-day dispatch method. In the planning stage, the sequence of plan is to determine the power of each storage device for a certain period, which is initially generated under the principle that PV arrays have the highest priority, followed by the batteries, and finally the P2H2P system, using short-term forecast data of both load and solar irradiance. The initial plan can be optimized with objectives of harvesting more PV generation in storage and minimizing unmet load through rescheduling P2H2P system and batteries. Three parameters including reserved capacity of batteries, predischarge coefficient of fuel cell (FC), and greedy coefficient of electrolyzer (EL), are introduced during plan optimization process to enhance the robustness against forecast errors. In the dispatching stage, the energy dispatch is subject to the scheduled plan and the operational constraints. To demonstrate the capabilities of the proposed strategy, a case study is performed for a hotel with a mean power consumption of 1567 kWh/day based on the system configuration optimized by HOMER software in comparison with the load following (LF) strategy and the global optimum solution solved by mixed integer linear programing (MILP). The simulation results show that the annual unmet load using the proposed strategy is reduced from 13,434 kWh to 2370 kWh, which is 528 kWh lower than the optimum solution. Meanwhile, the cost of energy (COE) of the proposed strategy decreases by US$ 0.08/kWh compared to the LF strategy and is equal to the optimum solution. Finally, the performance of configuration optimization employing genetic algorithm (GA) under different energy management strategies is investigated with the objective function of minimizing the net present cost (NPC). Furthermore, the robustness of the proposed strategy is studied. The results show that the proposed strategy gives an NPC and COE of US$ 2.4 million (Mn) and US$ 0.43/kWh, which are 23.4% and 9.7% lower than those of systems utilizing the SoC-based strategy and the LF strategy, respectively. The results also demonstrate that the strategy is robust against forecast errors, especially for overestimated forecast models.

Sizing and Energy Management of Parking Lots of Electric Vehicles Based on Battery Storage with Wind Resources in Distribution Network

In this paper, an optimal sizing and placement framework (OSPF) is performed for electric parking lots integrated with wind turbines in a 33-bus distribution network. The total objective function is defined as minimizing the total cost including the cost of grid power, cost of power losses, cost of charge and discharge of parking lots, cost of wind turbines as well as voltage deviations reduction. In the OSPF, optimization variables are selected as electric parking size and wind turbines, which have been determined optimally using an intelligent method named arithmetic optimization algorithm (AOA) inspired by arithmetic operators in mathematics. The load following strategy (LFS) is used for energy management in the OSPF. The OSPF is evaluated in three cases of the objective function such as minimizing the cost of power losses, minimizing the network voltage deviations, and minimizing the total objective function using the AOA. The capability of the AOA is compared with the well-known particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms for solving the OSPF in the last case. The findings show that the power loss, voltage deviations, and power purchased from the grid are reduced considerably based on the OSPF using the AOA. The results show the lowest total cost of energy and also minimum network voltage deviation (third case) by the AOA in comparison with the PSO and ABC with a higher convergence rate, which confirms the better capability of the proposed method. The results of the first and second cases show the high cost of power purchased from the main grid as well as the high total cost. Therefore, the comparison of different cases confirms that considering the cost index along with losses and voltage deviations causes a compromise between different objectives, and thus the cost of purchasing power from the main network is significantly reduced. Moreover, the voltage profile of the network improves, and also the minimum voltage of the network is also enhanced using the OSPF via the AOA.

Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis

In this paper, an optimized stand-alone hybrid energy system consists of photovoltaic (PV) arrays, wind turbines (WT), and battery (BA) storage (HPV/WT/BA) presented with the objective of total net present cost (TNPC) minimization subjected to loss of energy probability (LOEP) considering the effect of interest rate (IR) changes. The hybrid system optimization is done based on real annual data of irradiance, wind speed, temperature and demand of a remote site. The main goal of the optimization is optimal sizing of the hybrid system components as the number of PVs, WTs, and batteries and transferred power to load by the inverter with minimizing the TNPC and satisfying the LOEP. A new method named improved grasshopper optimization algorithm (IGOA) based on a nonlinearly decreasing inertia weight strategy is applied for the determination of optimal sizing of the system with the lowest TNPC and best LOEP. Energy management is adapted with the operation of the hybrid system based on load following strategy (LFS). The results showed that the proposed methodology based IGOA finds the HPV/BA system as an optimal combination to supply the site demand with the lowest TNPC and better reliability. The superiority of the IGOA is confirmed in comparison with conventional GOA and well-known particle swarm optimization (PSO) methods in achieving an optimal solution with lower cost and higher reliability. The results of considering IR changes effect are cleared that the LOEP and cost of energy (COE) are increased by 0.56% and 5.37%, respectively and TNPC is decreased by 1.66% due to 1% increasing the IR in optimization of the HPV/BA system. So, the results of interest rate changes indicate a significant effect on the system cost and reliability. Moreover, with increasing the storage capital cost, the TNPC and COE are increased and the reliability level is dropped.

Energy Management Strategies for PV/Diesel Hy-brid Systems in Remote Areas: Effects of “Flexy-Energy” Strategy

Energy management strategies are essential for the operation of hybrid energy systems. This paper focuses mainly on the implementation of a novel energy management strategy called Flexy-energy. A simulation tool was developed for power plants operating under this approach. The Operating range of load ratios (OpR) was used as an optimisation parameter to assess the performance of the strategy. The performance of the Flexy-energy strategy was then compared to the load following (LF) and cycle charging (CC) strategies, two strategies widely used in the management of hybrid energy systems. The Levelized Cost of Electricity (COE) is the main performance parameter considered in this paper. Two main outcomes have emerged from the present study. First, the Flexy-energy management strategy presents a lower COE comparatively to the LF and CC strategies. Second, the operating range of load ratios of diesel generators should be as large as possible for the Flexy-energy approach to minimise the COE. Indeed, under the Flexy-energy strategy, the scenario with an operating range of load ratios (OpR) within [25–100] has a COE of 9% lower than the COE of the case with an OpR within [80–100].

Optimization of an off-grid integrated hybrid renewable energy system with various energy storage technologies using different dispatch strategies

ABSTRACT A large population of India is living in villages, some of them are living in remote areas isolated from the grid. It is not feasible or economical to extend the grid connection to provide electricity for those villages, but an autonomous integrated hybrid renewable energy system can be a viable option. Hence, this study proposed to provide electricity and freshwater availability for the un-electrified off-grid villages of Odisha state in India with available renewable energy resources. In order to provide a continuous power supply, the study focused on two energy storage technologies, such as hydrogen and battery storage systems along with a diesel generator. In the context of battery technology, Lead Acid (LA), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered. To obtain an optimally configured Integrated Hybrid Renewable Energy System (IHRES), a total of four IHRES configurations are modeled such as Photo Voltaic Panels (PV)/Wind Turbines (WT)/Biomass Generator (BMG)/Diesel Generator (DG)/Ni-Fe, PV/WT/BMG/DG/LA, PV/WT/BMG/DG/Li-Ion, and PV/WT/BMG/DG/Hydrogen Storage System (HSS) and conducted a study with two dispatch strategies, such as load following (LF) and cycle charging (CC), using four metaheuristic algorithms in the MATLAB® environment. From the results, it is found that the proposed Salp Swarm Algorithm has proven its robustness and convergence efficiency when compared to other algorithms in finding the global best optimal values. The Ni-Fe battery-based IHRES with CC strategy provided a minimum life cycle cost (LCC) and cost of energies (COE) as $6,33,789 and 0.23793 $/kWh, respectively, and is considered as a base case. The other energy storage technology-based IHRESs with LA, Li-Ion, and HSSs are obtained an LCC and COEs respectively 74%, 161%, and 231% higher than the base case LCC and COEs. In view of the total annual fuel consumption and carbon emissions, it has shown its best performance with CC strategy compared to LF strategy. Finally, the sensitivity analysis has been conducted to the optimal configuration with variable input parameters, such as interest rate and diesel prices.

Optimization and Energy Management of Hybrid Photovoltaic-Diesel-Battery System to Pump and Desalinate Water at Isolated Regions

This research work aims to provide detailed feasibility, a techno-economic evaluation, and energy management of stand-alone hybrid photovoltaic-diesel-battery (PV/DG/B) system. The proposed system can be applied to supply a specific load that is far away from the utility grid (UG) connection, and it is located in Minya city, Egypt, as a real case study. The daily required desalinated water is 250 m 3 . The total brackish water demands are 350-500 m 3 and 250-300 m 3 of water in summer and winter seasons, respectively. Two different sizes of reverse osmosis (RO) units; RO-250 and RO-500, two energy control dispatch strategies; load following (LF) and cycle charging (CC); two sizes of DG; 5 kW and 10 kW are considered in the case study. The cost of energy, renewable fraction, environmental impact, and breakeven grid extension distance are the main criteria that have been considered to determine the optimal size of PV/DG/B to supply the load demand. HOMER ® software is used to perform the simulation and optimization. For this case study, the minimum cost of energy and the minimum total present cost are 0.074 $/kWh and 207676 $, respectively. This is achieved by using a RO-500 unit and a LF dispatch control strategy. The related sizes to the best option of PV/DG/B are 120 kW PV array, 10 kW DG, 64 batteries, and 50 kW converter. A comparison with grid extension and installing stand-alone diesel generation is also carried out. The results of comparison have confirmed that the grid connection is better than all considered options using the RO-250 unit. However, for the RO-500 unit, all options of hybrid PV/DG/B are more economically feasible compared with grid connection, and the best cost-effective option is the one including LF strategy with 10 kW DG. Stand-alone diesel generator produces 119110 kg/year and 117677 kg/year of CO2 respectively for RO-250 and RO-500.

Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy

In recent years, the concept of hybrid energy systems (HESs) is drawing more attention for electrification of isolated or energy-deficient areas. When optimally designed, HESs prove to be more reliable and economical than single energy source systems. This study examines the feasibility of a combined dispatch (CD) control strategy for a photovoltaic (PV)/diesel/battery HES by combining the load following (LF) strategy and cycle charging (CC) strategy. HOMER software is used as a tool for optimization analysis by investigating the techno-economic and environmental performance of the proposed system under the LF strategy, CC strategy, and combined dispatch CD strategy. The simulation results reveal that the CD strategy has a net present cost (NPC) and cost of energy (COE) values of $110,191 and $0.21/kWh, which are 20.6% and 4.8% lower than those of systems utilizing the LF and CC strategies, respectively. From an environmental point of view, the CD strategy also offers the best performance, with CO2 emissions of 27,678 kg/year. Moreover, the results show that variations in critical parameters, such as battery minimum state of charge, time step, solar radiation, diesel price, and load growth, exert considerable effects on the performance of the proposed system.

A Voltage and Frequency Control Strategy for Stand-Alone Full Converter Wind Energy Conversion Systems

This paper addresses the design and analysis of a voltage and frequency control (VFC) strategy for full converter (FC)-based wind energy conversion systems (WECSs) and its applicability for the supply of an isolated load. When supplying an isolated load, the role of the back-to-back converters in the FC must change with respect to a grid-connected application. Voltage and frequency are established by the FC line side converter (LSC), while the generator side converter (GSC) is responsible for maintaining constant voltage in the DC link. Thus, the roles of the converters in the WECS are inverted. Under such control strategies, the LSC will automatically supply the load power and hence, in order to maintain a stable operation of the WECS, the wind turbine (WT) power must also be controlled in a load-following strategy. The proposed VFC is fully modelled and a stability analysis is performed. Then, the operation of the WECS under the proposed VFC is simulated and tested on a real-time test bench, demonstrating the performance of the VFC for the isolated operation of the WECS.

Improved system control logic for an MCHP system incorporating electric storage

In this study, we assessed the performance and suitability of a novel control strategy for both a Mini Combined Heat and Power (MCHP) unit and a photovoltaic system, combined with thermal (TES) and electric (EES) energy storage systems. The newly developed control strategy incorporates a forecast for the photovoltaic system output throughout the day, coupled with a daily electric load projection. It also takes the current storage levels of the TES and the EES into account and identifies favourable EES system capacity set-points throughout the day. A simulation model of such a system was realised in Matlab and the performance of the new electric storage-following operational control compared to an identical system operated under a thermal load-following strategy. Furthermore, the investigated system was also analysed against a photovoltaic system with an EES, but without an MCHP unit.It was found that the degree of electric self-sufficiency was always higher in the system operated under the electric storage-following control strategy. Varying the size of the EES and the photovoltaic system, the highest degree of electric self-sufficiency (nearly 100%) was associated with the largest system configuration tested (16kWh EES combined with a 14kW photovoltaic system). Acceptable levels of self-sufficiency in excess of 95% were already reached in a system consisting of a 10kWh EES and a 10kW photovoltaic system. There is a strong indication that the specific daily scheduling of the MCHP unit combined with the anticipated daily PV system electricity output, advantageous energy storage levels and enhanced EES system capacity utilisation, clearly distinguish the novel electric storage-following control strategy from a thermal load-following operational control.

Energy control strategies for the Fuel Cell Hybrid Power Source under unknown load profile

Four new energy control strategies are proposed here for the Fuel Cell Hybrid Power Source (FCHPS) used in stationary and mobile FC application (such as the FC backup source for a smart-house and FC vehicle, respectively) based on the Load Following (LF) control and Maximum Efficiency Point Tracking (MEPT) control of the fueling rates. The LF control approach is used to design simple strategies of the Energy Management Unit (EMU) that will assure a charge-sustaining mode for the batteries stack of the Energy Storage System (ESS). If a fueling rate is controlled based on the LF strategy, then the other is controlled based on MEPT strategy in order to maximize the FC net power available. The advantages of the proposed EMU strategies during an unknown load cycle are comparatively shown.

Efficient energy control strategies for a Standalone Renewable/Fuel Cell Hybrid Power Source

In this paper, four energy control strategies are proposed and analyzed for the standalone Renewable/Fuel Cell Hybrid Power Source (RES/FC HPS). The concept of the load following (LF) and Maximum Efficiency Point Tracking (MEPT) is used to control the fueling rates. A standalone RES/FC HPS uses at least one Renewable Energy Sources (RES) and a Polymer Electrolyte Membrane (PEM) Fuel Cell (FC) as backup source. Photovoltaic (PV) array and wind turbines (WT) farm are used as RES and the surplus of energy during light load stages is stored in hydrogen tank via water electrolysis to fuel the PEMFC. Small-scale RESs and commercially available PEMFCs are interfaced to the common DC bus via power converters and then to the single-phase distribution grid through a voltage source inverter. RES/FC HPS seem to be an efficient alternative for supplying smart houses and isolated sites. This paper proposes a new supervision strategy of the Energy Management Unit (EMU) based on the LF control approach that assures a charge-sustaining (CS) mode for the Energy Storage System (ESS). So, the capacity of the batteries stack can be reduced at minimum if it is directly connected to the DC bus. The ultracapacitors stack compensates dynamically the power flow balance on the DC bus, regulating the DC voltage via a bidirectional buck-boost power converter. Thus, a semi-active hybrid topology is adopted for the ESS having the batteries stack. The MEPT loops ensure an optimized energy management of the RES/FC HPS. The LF strategy designed based on the power flow balance guarantees the load demand through the efficient management of the power flow from RES, FC and ESS, adapting FC energy production to load profile. The FC energy production is controlled via the fueling rates (one is controlled in the LF loop and the other is controlled in the MEPT loop) to maximize the energy consumption indicator: the ratio of the produced energy and the fuel consumed by the FC system during a load cycle. Thus, this paper evaluates the control performances of the four control topologies to fuel the FC stack during an variable RES and load profile. The MATLAB-Simulink® software package is used to model the RES/FC HPS and develop the four EMU strategies. Simulation results shown comparatively the performance of the EMU strategies proposed under different scenarios of RES power generation and load demand.

Predictive Dispatch Across Time of Hybrid Isolated Power Systems

The paper proposes a methodology for the optimal dispatch of energy sources in hybrid and isolated energy systems. The proposed approach is based on the formulation and solution of a nonlinear discrete optimization problem aimed at optimizing input and output time trajectories for a set of combined generating and storage technologies. Loads and interruptible loads are among controlled variables, and are modeled according to their interruption costs. The approach is general enough to be applied to any hybrid system configuration and was developed having in mind the complex hybrid system architectures comprising several competing storage technologies (battery, pumping, and hydrogen). Test results are aimed at showing the feasibility of the proposed methodology, comparing optimal trajectories to suboptimal system behavior given by load-following strategies.