A novel joint bidding technique for fuel cell wind turbine photovoltaic storage unit and demand response considering prediction models analysis Effect's

The stake of distributed generation resources like fuel cell in daily market is proved to be a major uncertain problem. The volatile character of market price together with the unbalanced nature of power can take hold of economic advancement of distributed generation resources which in turn can culminate in diversion retribution while the market is being struck. This study introduces a market participation model in share conditions to improve the profit for Fuel Cell/wind turbine/storage/photovoltaic and demand response. To solve the mentioned problem, an accurate prediction model is presented in this paper. This model is based on complete ensemble empirical mode decomposition, and multiple artificial neural network which is coupled with Broyden water cycle algorithm. By this algorithm, the prediction accuracy of proposed forecast engine is enhanced and could get the better results. A sure-footed stochastic optimization approach was deployed in order to take prices of markets and distributed generation resources into account. In the generation of distributed generation resources, forecasting error database in everyday, modified, and depressed market was drawn on to induce probabilistic scenario. Improbable variables were discarded by a neuro-fuzzy model. Eventually, to illustrate the joint model strategy suggested in the study, a testing system contains fuel cell/wind turbine/storage unit/photovoltaic and demand response was utilized and the attained results were calculated in two different periods.     

A mobile off-grid platform powered with photovoltaic/wind/battery/fuel cell hybrid power systems

Cross utilization of photovoltaic/wind/battery/fuel cell hybrid-power-system has been demonstrated to power an off-grid mobile living space. This concept shows that different renewable energy sources can be used simultaneously to power off-grid applications together with battery and hydrogen energy storage options. Photovoltaic (PV) and wind energy are used as primary sources and a fuel cell is used as backup power. A total of 2.7 kW energy production (wind and PV panels) along with 1.2 kW fuel cell power is supported with 17.2 kWh battery and 15 kWh hydrogen storage capacities. Supply/demand scenarios are prepared based on wind and solar data for Istanbul. Primary energy sources supply load and charge batteries. When there is energy excess, it is used to electrolyse water for hydrogen production, which in turn can either be used to power fuel cells or burnt as fuel by the hydrogen cooker. Power-to-gas and gas-to-power schemes are effectively utilized and shown in this study. Power demand by the installed equipment is supplied by batteries if no renewable energy is available. If there is high demand beyond battery capacity, fuel cell supplies energy in parallel. Automatic and manual controllable hydraulic systems are designed and installed to increase the photovoltaic efficiency by vertical axis control, to lift up & down wind turbine and to prevent vibrations on vehicle. Automatic control, data acquisition, monitoring, telemetry hardware and software are established. In order to increase public awareness of renewable energy sources and its applications, system has been demonstrated in various exhibitions, conferences, energy forums, universities, governmental and nongovernmental organizations in Turkey, Austria, United Arab Emirates and Romania.     

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.     

A novel forecasting method based on the economic and demand response for FC/WT/PV unit and a 3 in 1 TES energy storage

Due to the lack of distribution resources and increasing demand in the daily market, the use of renewable resources is increasing. But renewable sources and market prices are uncertain behavior and cause economic problems. This paper introduces a novel market participation model include wind turbine, photovoltaic, fuel cell integrated with a novel hybrid TES energy storage system (3 in 1 concept) to minimize cost and improve load demand reliability. Also, to solve he mentioned problem a novel forecasting method are proposed. This model is a new multi artificial neural network based on the complete ensemble empirical mode decomposition which is coupled with Tanh function and using RMSE, MAPE and NMAE method the error rate of the proposed method is calculated. By using this method, the forecasting accuracy is improved and also with a novel energy storage the economic issue and market reliability are improved. Also, using the stochastic model the uncertainty system's behavior are modeled to obtain an accurate results of market participation and increase demand supply. Finally, a testing system includes wind turbine/photovoltaic/fuel cell/storage system and demand response are used to prove the superiority of the proposed model in comparison to other models.     

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

This paper presents an integrated operation model of renewable energy, distributed generators (DGs), energy storage, and demand response to overcome the challenges of renewable energy market participation. Additionally, an accurate prediction model for wind, photovoltaic (PV) power, and market price is introduced based on ensemble empirical model decomposition. In this model, radial basis function neural network (RBFNN) is used for the forecasting of subsignals from the analysis model. The training model of RBFNN is shaped based on the B-water cycle algorithm (WCA) optimization method. Additionally, by considering the adjusted market, demand response, and uncertainties analysis, this method reduced the economic damages of generators and loads. A case study test has consisted of a wind turbine, photovoltaic energy, fuel cell energy, demand response, and energy storage.     

基于双模糊控制的独立微网能量优化策略

为使微网运行效益最大化,提出一种含风—储系统的独立微网的能量优化策略,该策略采用双层模糊控制方式,针对微网峰谷特性,根据日前启停机计划确定风电机组与需求侧管理负荷的投切状态,对实时调度则使用模糊控制得到风电机组、储能与负荷的功率值。对于微网瞬时功率波动,采用模糊理论,通过蓄电池—需求侧负荷混合系统平抑功率波动。实例应用结果表明,该独立微网能量优化策略有效。     

Distributed control of a user-on-demand renewable-energy power-source system using battery and hydrogen hybrid energy-storage devices

A user-on-demand power source based on renewable energy requires storage devices to balance power sources and power demands because of the fluctuation of power sources like solar cells or wind power generators. The role of the control system is defined as two different tasks: allowing a power-flow imbalance between demand and power sources; and balancing the power flow inside the system. Since this control is complicated, many control methods using precise calculation of the power balance have been proposed. An analogue-like distributed control method - named “modified DC-bus signalling” - for controlling a renewable-energy power source without the need for a central processing unit is proposed. The modified DC bus signalling method discussed in this paper is composed of a DC-bus line connected with a battery, water-splitting electrochemical cell, and a fuel cell for hydrogen-energy storage via converters. The proposed control method was demonstrated to be able to control step-like and random changes in input and output power. The battery compensated high-frequency fluctuations in power demand, and the electrochemical cell and fuel cell handled the remaining low-frequency ones, which were matched to their response speeds.     

基于负荷侧响应的含储热热电联产的风电消纳模型

为了有效减少弃风,提高风电消纳能力,该文从负荷侧出发,通过峰谷分时电价策略引导用户的用电方式,达到削峰填谷,优化负荷曲线的目的。同时,在传统热电联产机组中应用大容量储热装置,通过对储热环节的控制,解耦“以热定电”约束,提高系统调节能力。以系统煤耗量最低为目标,构建包含储热的热电联产机组与风电联合出力优化调度模型。该模型考虑系统中的含储热热电联产机组运行成本,同时兼顾储热、负荷侧响应与热电平衡的相关约束等因素,采用基于模拟退火的粒子群算法对模型进行求解,并利用算例比较不同模式下的结果,验证了模型的有效性。     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

Optimizing hydrogen production capacity and day ahead market bidding for a wind farm in Texas

Producing green hydrogen from wind energy is one potential method to mitigate curtailment. This study develops a general approach to examine the economic benefit of adding hydrogen production capacity through water electrolysis along with the fuel cell and storage facilities in a wind farm in north Texas. The study also investigates different day ahead market bidding strategies in the existence of these technologies. The results show that adding hydrogen capacity to the wind farm is profitable when hydrogen price is greater than $3.58/kg, and that the optimal day ahead market bidding strategy changes as hydrogen price changes. The results also suggest that both the addition of a fuel cell to reconvert stored hydrogen to electricity and the addition of a battery to smooth the electricity input to the electrolyzer are suboptimal for the system in the case of this study. The profit of a particular bidding scenario is most sensitive to the selling price of hydrogen, and then the input parameters of the electrolyzer. This study also provides policy implications by investigating the impact of different policy schemes on the optimal hydrogen production level.     

Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid

This paper deals with an optimal battery energy storage capacity for the smart grid operation. Distributed renewable generator and conventional thermal generator are considered as the power generation sources for the smart grid. Usually, a battery energy storage system (BESS) is used to satisfy the transmission constraints but installation cost of battery energy storage is very high. Sometimes, it is not possible to install a large capacity of the BESS. On the other hand, the competition of the electricity market has been increased due to the deregulation and liberalization of the power market. Therefore, the power companies are required to reduce the generation cost in order to maximize the profit. In this paper, a thermal units commitment program considers the demand response system to satisfy the transmission constraints. The BESS capacity can be reduced by the demand response system. The electric vehicle (EV) and heat pump (HP) in the smart house are considered as the controllable loads of the demand side. The effectiveness of the proposed method is validated by extensive simulation results which ensure the reduction of BESS capacity and power generation cost, and satisfy the transmission constraints.     

Dynamic modeling and simulation of a small wind–fuel cell hybrid energy system

This paper describes dynamic modeling and simulation results of a small wind–fuel cell hybrid energy system. The system consists of a 400 W wind turbine, a proton exchange membrane fuel cell (PEMFC), ultracapacitors, an electrolyzer, and a power converter. The output fluctuation of the wind turbine due to wind speed variation is reduced using a fuel cell stack. The load is supplied from the wind turbine with a fuel cell working in parallel. Excess wind energy when available is converted to hydrogen using an electrolyzer for later use in the fuel cell. Ultracapacitors and a power converter unit are proposed to minimize voltage fluctuations in the system and generate AC voltage. Dynamic modeling of various components of this small isolated system is presented. Dynamic aspects of temperature variation and double layer capacitance of the fuel cell are also included. PID type controllers are used to control the fuel cell system. SIMULINK^TM is used for the simulation of this highly nonlinear hybrid energy system. System dynamics are studied to determine the voltage variation throughout the system. Transient responses of the system to step changes in the load current and wind speed in a number of possible situations are presented. Analysis of simulation results and limitations of the wind–fuel cell hybrid energy system are discussed. The voltage variation at the output was found to be within the acceptable range. The proposed system does not need conventional battery storage. It may be used for off-grid power generation in remote communities.     

Operation planning of hydrogen storage connected to wind power operating in a power market

In this paper, a methodology for the operation of a hybrid plant with wind power and hydrogen storage is presented. Hydrogen produced from electrolysis is used for power generation in a stationary fuel cell and as fuel for vehicles. Forecasts of wind power are used for maximizing the expected profit from power exchange in a day-ahead market, also taking into account a penalty cost for unprovided hydrogen demand. During online operation, a receding horizon strategy is applied to determine the setpoints for the electrolyzer power and the fuel cell power. Results from three case studies of a combined wind-hydrogen plant are presented. In the first two cases, the plant is assumed to be operating in a power market dominated by thermal and hydropower, respectively. The third case demonstrates that the operating principles are also useful for isolated wind-hydrogen systems with backup generation.     

An off-peak energy storage concept for electric utilities: Part I—Electric utility requirements

The water battery, a reversible water electrolyser device being developed in a long-term research effort at Battelle's Columbus Laboratories, was evaluated in an analytical and conceptual design study as a load-levelling system for an electric utility. During periods when off-peak electrical power was available, the water battery would produce hydrogen and oxygen by electrolysis of water; during peak demand periods the water battery would be operated in the reverse mode, functioning as a fuel cell by producing electrical power through the recombination of the oxygen and hydrogen held in its storage vessels.The analysis involved characterisation of the PSE&G system demand requirements now and in the future, its current off-peak energy availability, the typical sizing and placement of energy storage units and the approximate break even economics and potential advantages to the utility of a water battery energy storage system. In the economic analysis, the water battery was compared with the gas turbine and the fuel cell for cost effectiveness in meeting peak and intermediate power demands, respectively.Compared with a ‘reformer-type’ fuel cell (costed at $300/kW for intermediate duty) the break even capital cost of a 50% efficient water battery would be $100/kW plus about $200/kW for each increase of $1/10⁶ Btu above the reference cost of $1/10⁶ Btu for fossil fuel. The available margin would increase about $50/kW for each decrease of 1 mill/kWh in off-peak energy cost below the reference cost of 8 mills/kWh. In a similar comparison with the gas turbine (costed at $135/kW) for peaking duty, the break even cost of a 50% efficient water battery would be $100/kW. The break even cost could rise about $100/kW for each increase in fossil fuel cost of $1/10⁶ Btu and about $20/kW for each decrease in off-peak energy cost of 1 mill/kWh.     

Optimization‐based power management of a wind farm with battery storage

This paper presents an optimization‐based control strategy for the power management of a wind farm with battery storage. The strategy seeks to minimize the error between the power delivered by the wind farm with battery storage and the power demand from an operator. In addition, the strategy attempts to maximize battery life. The control strategy has two main stages. The first stage produces a family of control solutions that minimize the power error subject to the battery constraints over an optimization horizon. These solutions are parameterized by a given value for the state of charge at the end of the optimization horizon. The second stage screens the family of control solutions to select one attaining an optimal balance between power error and battery life. The battery life model used in this stage is a weighted Amp‐hour throughput model. The control strategy is modular, allowing for more sophisticated optimization models in the first stage or more elaborate battery life models in the second stage. The strategy is implemented in real time in the framework of model predictive control. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran

In this paper a novel intelligent method is applied to the problem of sizing in a hybrid power system such that the demand of residential area is met. This study is performed for Kahnouj area in south-east Iran. It is to mention that there are many similar regions around the world with this typical situation that can be expanded. The system consists of fuel cells, some wind units, some electrolyzers, a reformer, an anaerobic reactor and some hydrogen tanks. The system is assumed to be stand-alone and uses the biomass as an available energy resource. In this system, the hydrogen produced by the reformer is delivered to the fuel cell directly. When the power produced by the wind turbine plus power produced by the fuel cell (fed by the reformer) are more than the demand, the remainder is delivered to the electrolyzer. In contrast, when the power produced by the wind turbine plus that produced by the fuel cell (fed by the reformer) are less than the demand, some more fuel cells are employed and they are fed by the stored hydrogen. Our aim is to minimize the total costs of the system such that the demand is met. PSO algorithm is used for optimal sizing of system's components.     

社区型能源互联网下的虚拟电厂参与电力市场策略分析

  [目的]  为了减少风能和太阳能为代表的DERs产能间歇性、随机性、不可控的影响,以及参与电力市场竞争出现不利因素,提出了社区型能源互联网模型。  [方法]  考虑了由风电场、光伏电站和负荷组成的虚拟电厂(virtual power plant, VPP)集群所构成的社区型能源互联网模型,将单VPP进行互联,在满足单VPP内部供需平衡基础上,将盈余电量通过VPP集群进行博弈共享,最后与电力市场进行交易,实现逆向售电。  [结果]  模型实现了社区型能源互联网利益最大化目标,同时加上需求响应(demand response, DR)实现用户节约使用电费的目的。  [结论]  通过社区能源互联网示范工程,提出适合VPP运行策略模型,通过建立的收益在不同场景下验证发用电策略的可行性,为社区能源互联网的建设及交易指明方向。     

Optimization of powerplant component size on board a fuel cell/battery hybrid bus for fuel economy and system durability

The size of the individual powerplant components on board a fuel cell/battery hybrid vehicle affects the power management strategy which determines both the fuel economy and the durability of the fuel cell and the battery, and thus the average lifetime cost of the vehicle. Cost is one of the major barriers to the commercialization of fuel cell vehicles, therefore it is important to study how the sizing configuration affects overall vehicle cost. In this paper, degradation models for the fuel cell and the battery on board a fuel cell/battery hybrid bus are incorporated into the power management system to extend their lifetimes. Different sizing configurations were studied and the results reveal that the optimal size with highest lifetime and lowest average cost is highly dependent on the drive cycle. The vehicle equipped with a small fuel cell stack serving as a range extender will fail earlier and consume more fuel under drive cycles with high average power demand resulting in higher overall cost. However, the same configuration gives optimal results under a standard bus cycle with lower average power demand. At the other end of the spectrum, a fuel cell-dominant bus does not guarantee longer lifetime since the fuel cell operates mostly under low-load conditions which correspond to higher potentials reducing lifetime. Such a configuration also incurs a higher initial capital cost of the fuel cell stack resulting in a high average cost. The best configuration is a battery-dominated system with moderately-sized fuel cell stack which achieves the longest lifetime combined with the lowest average running cost throughout the lifetime of the vehicle.     

The optimization of hybrid energy conversion systems using the dynamic programming model—Rapsody

The paper describes a stochastic, dynamic programming model, RAPSODY, which is designed to analyse and determine optimal operating strategies for a hybrid electricity generating system comprising up to three diesel sets with optional battery storage and augmented by variable wind or photo-voltaic power. The model takes capital, operating and maintenance, and fuel costs into account to assess the optimal operating strategy for the auxiliary and to calculate the average daily cost of satisfying an electrical load profile which may also contain a stochastic element. In doing so, the decreased fuel efficiency and lifetime of the diesel set as it is operated below full capacity is explicitly taken into consideration as is a further important cost component attributable to switching on. The model is provided with an efficient optimizing routine which allows the user to obtain optimal component sizes for a particular load profile and wind or solar resource. An example is given in which a hybrid wind power system incorporating battery storage and an auxiliary diesel generator is optimized. For the case studied, the auxiliary switching cost and the shape of its operating cost function were important in defining the optimal operating strategy which was a significant factor in minimizing generating costs.     

Stand-alone micro-trigeneration system coupling electrolyzer,fuel cell,and heat pump with renewables

A micro hydrogen system in conjunction with renewable energy, namely a wind turbine, a photovoltaic array, and an air-source heat pump, is designed to satisfy the power, heating, and cooling needs of a stand-alone household in a Mediterranean climate. An hourly-based model is used to simulate its operation throughout the year. A unique power management strategy is applied to achieve optimum configuration and size of the components without shortage or excess energy. Unlike previous practices, there is no release of excess heat into the environment. An innovative combination of a fuel cell and a heat pump followed the household's electrical and thermal (domestic hot water/heating and space cooling) profile. Almost 80% of the energy for preparing hot water and household cooling/heating was obtained from waste heat from these devices. The system is compared to the most commonly used stand-alone hybrid renewable energy system with battery storage. The hydrogen system needs four time less batteries and it does not need a back-up diesel generator. Although the energy storage in batteries is more efficient than in hydrogen, the hydrogen system requires only 10% larger primary energy input than the system with only battery storage.