Performance analysis of a hybrid solar-hydrogen-retired EV batteries (REVB) energy system with thermal-electrical loops

Utilizing solar energy is an efficient method to provide hybrid renewable energy system with sufficient thermal/electrical energy. Meanwhile, the rapid development of electrical vehicles leads to an excess of retired electric vehicles. As a combination of the abovementioned two conceptions, this study proposed and examined a hybrid solar-hydrogen-retired electrical vehicle battery energy system to meet thermal and electrical loads for small-scale usage. The novelty of this research is delivered as follows: first of all, the proposed hybrid energy system supplies both thermal and electrical energy to small-scale end users; secondly, the retired electrical vehicle batteries are recycling to relieve the pressure of battery demand; thirdly, an energy management strategy to regulate the complicated hybrid energy system is designed. The results show that with assistance of fuel cell as an energy storage unit, solar energy can basically satisfy the annual thermal/electrical load with maximum monthly energy supplement of 1220.43 MJ and 1572.75 kWh, respectively. However, the solar radiation serving as single energy source is not very reliable for large-scale utilization. Although the state of charge does not fluctuate greatly, the small range charge/discharge between 59% and 63% can still guarantee the normal operation of the proposed hybrid energy system.     

Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty

In view of the intermittency and uncertainty associated with both the electricity production sector of restructured power system and their competitive markets, it is necessary to develop an appropriate risk managing scheme. So that it is desirable to trade-off between optimum utilization of intermittent generation resources (i.e. renewable energy resources), uncertain market prices and related risks in order to maximize participants' benefits and minimize the corresponding risks in the multi-product market environment. The main goal of this paper is to investigate risk management by introducing a novel multi-risk index to quantify expected downside risk (EDR) which is caused by both the wind power and market price uncertainties. Value-at-Risk (VaR) method is used to assess the mentioned risk issue by the proposed weighted EDR, so that an optimal trade-off between the profit and risk is made for the system operations. Also, the roulette wheel mechanism is employed for random market price scenario generation wherein the stochastic procedure is converted into its respective deterministic equivalents. Moreover, the autoregressive integrated moving average (ARIMA) model is employed to characterize the stochastic wind farm (WF) generation by predetermined mean level and standard deviation of wind behavior as well as temporal correlation. The problem is formulated as a mixed-integer stochastic framework for a hydro-wind power system scheduling and tested on a generation company (GENCO).     

Design of a cost-effective on-grid hybrid wind–hydrogen based CHP system using a modified heuristic approach

An economic model and optimization procedure is developed in this paper for grid-connected hybrid wind–hydrogen combined heat and power systems for residential applications in northeastern Iran. The model considers various significant factors: energy production cost, electrical trade with local grid, electrical power generation from the wind/hydrogen energy system, thermal recovery from the fuel cell, and maintenance. Also, various tariffs for purchasing and selling electrical energy from the local grid are considered for the hybrid system operation. The optimization objective is to minimize the system total cost subject to relevant constraints for residential applications. To achieve this aim, an efficient optimization method is proposed based on particle swarm optimization. The proposed algorithm performance is compared with that for the imperialist competition algorithm. The results show that the hybrid system is the most cost-effective for the residential load, and the results of the proposed algorithm are more promising than those for the alternative algorithm.     

Risk-based performance of power-to-gas storage technology integrated with energy hub system regarding downside risk constrained approach

In modern power systems, the reliability of energy supplies is a real challenge for the operators. The emergence of renewable energy resources, along with multi-career users, requires multi-career systems. In this regard, the energy hub (EH) as an integrated system can be used to increase the reliability of the system. The power-to-gas (P2G) and P2G storage are two practical technologies to achieve high efficiency in energy systems. In this paper, the contribution is optimal scheduling of stochastic problem in EH system amalgamated with CHP unit, P2G storage, thermal storage, boiler, wind power, and electrical storage to supply the heat, gas, and power loads by regarding demand response program (DRP). For the electrical loads, the load shifting strategy is considered to minimize the operational cost of the EH system. In order to manage related uncertainties about electricity price, wind power, and electrical loads, the downside risk constraint (DRC) method is applied to investigate the EH system function. According to the obtained results, by increasing approximately 2.8% of the operational cost, the risk level can be reduced remarkably. And also, almost 10% of the energy shifted from peak hours to the off-peak time after DRP is applied.     

Smart house-based optimal operation of thermal unit commitment for a smart grid considering transmission constraints

This paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.     

Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming

Penetration of renewable energy sources (RESs) in power systems increase all over the world to overcome current challenges, most importantly environmental issues. Beside advantages of RESs, their integration into the power systems have imposed various challenges considering uncertain and intermitted power output. To cope with these challenges, utilizing energy storage systems with renewable energy sources alongside the demand response (DR) programs are considered as reliable solutions. On the other hand, in an uncertain environment, minimizing worst-case cost or regret is counted as an important criterion to evaluate operation of any system under uncertain parameters. Therefore, in this paper, optimal operation of power systems is solved under penetration of wind turbines, hydrogen storage system, and DR programs in an uncertain environment. To guarantee robust operation of the system under the worst-case scenario, a novel stochastic p-robust optimization method (SPROM) is proposed which combines both stochastic programming and robust optimization approaches where minimizes the worst-case cost or regret level. The performance of the developed model is evaluated considering a six-bus test system under two cases as stochastic optimization (SO) and SPROM. Obtained results show that the maximum regret level is reduced considerably using the proposed SPROM comparing with pure stochastic method.     

Fast and stable operation approach of ship solid oxide fuel cell-gas turbine hybrid system under uncertain factors

This work focuses on investigating the adaptability of solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system for ship application under uncertain factors. The effect of rapids, wind and waves on the performance of ship SOFC-GT is analyzed. In addition, a novel control system combining fuzzy logic theory, temperature feedforward and coordination factor on-line adjustment is proposed to address the problem of load disturbances caused by uncertain factors. The results show that the proposed operation strategy can shorten the thermal response time inside fuel cell stacks by almost 49.97%, meanwhile, reducing the maximum temperature changing rate at the electrochemically active tri-layer cell composed of anode, electrolyte, and cathode (PEN structure) by around 17.86%. Moreover, the reasonable matching between air flow and fuel flow is an essential prerequisite to ensure the safe and efficient operation of ship SOFC-GT. While the SOFC-GT is working at full load, the results indicate that the fuel to air ratio cannot exceed 2.56?10^?2 g/g. Finally, an application scenario of the 5000-ton river-to-sea cargo ship sails from Nanjing Port to Yangshan Port (Eastern China) is conducted to analyze the operation characteristics of ship SOFC-GT under uncertain factors. Two set of 1000 kW SOFC-GT systems with the electrical efficiency of 64.66% is designed for the target ship, the results conclude that the operation strategy of each SOFC-GT system supports 50% load is beneficial in reducing the power tracking time and SOFC temperature overshoot. The average electrical efficiency of 61.45% and 61.04% are achieved in winter and summer typical days respectively in the whole voyage.     

Risk-averse stochastic operation of a power system integrated with hydrogen storage system and wind generation in the presence of demand response program

Wind generation (WG) units as renewable energy sources (RESs) are increasing in the world due to environmental functions and lack of conventional energy sources. Also, hydrogen storage system (HSS) as an energy storage system (ESS) is used to cope with variable nature of RESs in which the concepts of power to hydrogen (P2H) and hydrogen to power (H2P) are defined. In this work, a risk-averse stochastic operation of HSS and WG is modeled using a scenario-based stochastic approach by considering price-responsive demand response (DR) program. All uncertainties are modeled via a scenario-based stochastic approach while the risk related uncertainties are modeled via the downside risk constraints (DRC) to capture the risk-averse operation of the HSS and WG. In order to investigate the impact of DRC implementation, a risk-averse strategy is compared versus risk-neutral strategy. Compared results show that the risk-in-cost (RIC) is reduced while the expected operation cost (EOC) is raised to deal with the risk of the uncertainty.     

合同能源管理(EMC)项目风险分析及应对策略

合同能源管理(EMC)是一种将能源节约与市场机制有效结合的节能模式。介绍了EMC的三种基本运作模式:节能量保证模式、节能效益分享模式和能源费用托管模式。目前我国节能服务产业迅速发展,政府制定并出台了多项政策支持和推进EMC机制的推广、应用。从政策风险、市场风险、融资风险、经营风险和信用风险等方面分析了EMC项目所面临的主要风险。提出了加强EMC项目风险管理的主要对策:进一步完善相关的法律、法规,加强经济激励手段,在资金方面为节能项目提供政策支持;扶持节能服务公司,提高技术实力,对发展较好的EMC管理机构加强指导,树立EMC管理模式典型;扩展融资渠道,搭建交易平台,设立专项风险投资基金,对进入节能服务领域的金融机构风险投资机构等进行鼓励;建立节能服务公司与节能企业的双向信用档案,完善评价标准。     

Cogeneration planning under uncertainty: Part I: Multiple time frame approach

Cogeneration system planning spans a multi-year time interval and is affected by various sources of uncertainty, mainly depending on the evolution of energy loads and prices. The high level of uncertainty requires assessing the convenience of adopting predefined technological alternatives in different scenarios of variation of the uncertain variables. This paper introduces an original framework based on identifying the characteristics of small-scale and large-scale uncertainties, whereby a comprehensive approach based on multiple (long-, medium- and short-term) time frames is formulated. Medium-term time periods exhibiting small variations of both electrical and thermal load patterns are grouped together and represented through electrical/thermal load and electricity price correlated random variables (RVs). A Monte Carlo simulation of the cogeneration plant operation is carried out in the short-term by extracting the RVs for each group from multivariate Normal probability distributions. Multi-year scenarios in the long-term time frame are addressed in the companion paper (Part II). The proposed approach is applied to a real energy system.     

基于条件期望价值的风险规避调度方法

短期风水火电联合系统经济调度对于减少化石燃料消耗、提高清洁能源利用效率具有重要意义,然而风电的随机不确定性、水电的多级耦合性及火电的出力非线性等给电力系统经济调度带来新的挑战。对此,综合考虑风电不确定性的风险和效益,从风险规避的角度构建了基于条件期望价值的风险规避调度模型。在此基础上,考虑调度模型多模态、非线性、复杂约束的特性,利用进化捕食策略对该模型进行求解。算例仿真表明,所提模型能够从风险与效益两个维度可靠地度量短期风水火电联合系统调度,获得最优调度方案,以保证系统运行的可靠性、经济性,以及系统消纳可再生能源的能力。     

Theoretical and experimental investigation of the performance of a desiccant air-conditioning system

Solid desiccant air-conditioning systems present an interesting alternative solution with regard to the conventional vapour compression systems, in the sense that they do not use any refrigerants and they present the opportunity to exploit thermal energy, and more specifically solar thermal energy, instead of electrical energy. In the present work a theoretical model is presented for the operation of a desiccant air-conditioning system, developed on the basis of existing approaches for the modelling of the main subsystems of such a device. The model is experimentally validated on a real scale system, through the exploitation of a significant number of measurements, which correspond to a typical range of operation conditions for these systems. The proposed model is used for the investigation of the performance of a system with a typical set-up, examining the influence of parameters such as the weather conditions, the level of the imposed cooling load, the efficiency level of the main subsystems of the set-up, the air flow rate and the regeneration temperature. The results confirm the potential of the examined technology to satisfy actual cooling loads, and at the same time they lead to specific conclusions for the operation of these systems.     

Design and simulation of the PV/PEM fuel cell based hybrid energy system using MATLAB/Simulink for greenhouse application

In this study, design and optimization of the hybrid renewable energy system consisting of Photovoltaic (PV)/Electrolyzer/Proton Exchange Membrane Fuel Cell (PEMFC) was investigated to provide electricity and heat for Greenhouse in ?anl?urfa (Turkey). The coupling of a photovoltaic system with PEMFC was preferred to supply continuous production of electric energy throughout the year. Additionally, produced heat from PEMFC was used to heating of the greenhouse by micro cogeneration application. The MATLAB/Simulink was applied to the design and optimization of the proposed hybrid system. In the designed system, solar energy was selected to produce the Hydrogen (H₂) required to run the electrolyzer. In cases where the solar energy is not sufficient and cannot meet the electricity requirement for the electrolyzer; the H₂ requirement for the operation of the PEMFC was met from the H₂ storage tanks and energy continuity was ensured. The electrolyzer was designed for H₂ demand of the 3 kW PEMFC which were met the greenhouse energy requirement. PEMFC based hybrid system has 48% electrical and 45% thermal efficiencies. According to optimization results obtained for the proposed hybrid system, the levelized cost of energy was found 0.117 $/kWh. The obtained results show the proposed PV/Electrolyzer/PEMFC hybrid power system provides an applicable option for powering stand-alone application in a self-sustainable expedient.     

Probabilistic hydrogen,thermal and electrical management of PEM-fuel cell power plants in distribution networks

This paper presents a Stochastic Multi-objective Optimal Operation Management (SMOOM) framework of distribution networks in presence of PEM-Fuel Cell Power Plants (FCPPs) and boilers. Operational costs, thermal recovery, power trade with grid and hydrogen management strategies are considered in this model. Furthermore, four objective functions has been considered as criteria for SMOOM, i.e. electrical energy losses, voltages deviations from their nominal values, total emissions emitted by CHP systems and grids, and total operational costs of CHP systems, as well as electrical energy cost of grids. A 2m + 1 Point Estimated Method is used to cope with the uncertain variables i.e. electrical and thermal loads, gas price of FCPPs consumption, fuel cost of residential loads, purchasing and selling tariff of electricity, hydrogen price, operation temperature of fuel cell stack, and the pressures of hydrogen and oxygen of anode and cathode, respectively. A new multi-objective Modified Firefly Algorithm (MFA) is implemented for minimizing the objective functions while the operational constraints are satisfied. Finally, a 69-bus distribution network is utilized to examine the performance of the proposed strategy regarding the rest.     

Energy dispatch schedule optimization and cost benefit analysis for grid-connected,photovoltaic-battery storage systems

A linear programming (LP) routine was implemented to model optimal energy storage dispatch schedules for peak net load management and demand charge minimization in a grid-connected, combined photovoltaic-battery storage system (PV+ system). The LP leverages PV power output and load forecasts to minimize peak loads subject to elementary dynamical and electrical constraints of the PV+ system. Battery charge/discharge were simulated over a range of two PV+ system parameters (battery storage capacity and peak load reduction target) to obtain energy cost for a time-of-use pricing schedule and the net present value (NPV) of the battery storage system. The financial benefits of our optimized energy dispatch schedule were compared with basic off-peak charging/on-peak discharging and real-time load response dispatch strategies that did not use any forecast information. The NPV of the battery array increased significantly when the battery was operated on the optimized schedule compared to the off-peak/on-peak and real time dispatch schedules. These trends were attributed to increased battery lifetime and reduced demand charges attained under the optimized dispatch strategy. Our results show that Lithium-ion batteries can be a financially viable energy storage solution in demand side, energy cost management applications at an installed cost of about $400–$500 per kW h (approximately 40–50% of 2011 market prices). The financial value of forecasting in energy storage dispatch optimization was calculated as a function of battery capacity ratio.     

考虑多源负荷不确定性的电力系统安稳风险优化

针对多源负荷的波动性和不确定性影响电力系统安稳运行,导致系统运行可靠性降低等问题,文章提出了一种考虑多源负荷不确定性的电力系统安稳风险优化方法;建立了考虑多源负荷不确定性的电力系统元件停运概率模型;提出了考虑多源负荷不确定性的电力系统安稳风险评估方法、安稳风险评估指标及其评估流程,对多源负荷波动下的电力系统安稳风险进行了评估。以系统运行过程安稳风险最低、负荷削减总量最小和系统经济损失后果最小为目标,建立电力系统安全稳定风险优化模型,对电力系统安稳风险进行优化。文章以IEEE-39节点算例进行仿真,验证了所建立模型的有效性。     

A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn.     

A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

To full use clean energy to meet load demand of electrical and thermal, the paper proposed a novel concept of virtual energy plant (VEP) including wind power plant (WPP), photovoltaic power generation (PV), combined heat and power generation (CHP), solar collectors (SC), electric boiler (EB), heat storage tank (HSK), and incentive‐based demand response (IBDR). Firstly, the basic structure of VEP is designed, including three subsystems, namely, electricity, heating, and energy storage. Then, a basic scheduling model is constructed under the objective of maximizing operating revenue without considering uncertainty. Thirdly, the conditional value at risk (CVaR) method and the robust optimization theory are used to handle the uncertainty factors in objective functions and condition constraints, and the risk aversion scheduling model is proposed. Finally, industrial park group in northern China are chosen, for example, analysis results show (1) VEP could convert the abandoned clean energy, use HSK to store heating energy during the valley load period, and supply heating energy in the peak period to obtain the excess economic benefits. (2) Lower‐prediction accuracy will amplify the uncertainty risk, when the robust β∈[0.8,0.825]&(0.925,1], the increase of confidence level β will lead to larger increase in CVaR. Especially when β∈(0.925,1), decision makers are extremely disgusted with the risks brought by the uncertainty factors, and correspondingly, the output of clean energy becomes minimum. (3) When the capacity ratio of HSK, EB and the electricity price of peak, valley are lower than 3, the values of revenue, VaR and CVaR change faster, but the ratios are larger than 3, the values change slower, which indicates that the scale of HSK capacity needs to be properly controlled to optimize the use of clean energy, and price‐based demand response could improve the operation profit while controlling risk properly. In general, the proposed scheduling model can maximize the use of clean energy to obtain economic benefits while rationally controlling risks.     

Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method

Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.     

Risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles using downside risk constraints method

Today, the utilizations of hydrogen storage systems (HSS), renewable generation units (PV and wind generation) and distributed energy units are increased in the intelligent parking lots (IPL) in order to charge the electric vehicles (EVs) with clean energy sources. In this work, the uncertainties of upstream grid price, the demand of IPL, wind speed, solar irradiation and temperature are modeled via scenario approach based on stochastic programing. Furthermore, the downside risk constraints method (DRCM) is applied to consider risk related to uncertainties to get risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles. The proposed risk-based formulation is modeled using mixed-integer linear programming (MIP) which is implemented under GAMS software and solved via CPLEX solver. Two cases namely risk-averse and risk-neutral strategies are studied and compared to show the effects of DRCM implementation. The obtained results demonstrate the expected performance cost (EPC) of IPL is slowly raised while risk-in-cost (RIC) is significantly reduced due to model of risk related to uncertainties.