考虑源荷双侧不确定的孤岛型微网能量管理优化模型

鉴于源侧风电出力和负荷侧预测误差的双重不确定性对微电网规划运行的影响愈发显著,采取分布式鲁棒优化联合机会约束方法对孤岛型微电网进行能量管理优化,降低系统运行成本,通过历史数据将风电出力和负荷侧预测误差转化为盒式模糊集,将机会约束转化为易求解的形式,建立了混合整数二阶锥规划模型。通过孤岛运行的微网算例分析及与其他优化方法的对比求解,验证了所提模型和求解策略的有效性。     

An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system

Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.     

Hydrogen distribution in the refinery using mathematical modeling

The increased requirement of hydrogen in the refinery is fulfilled by proper distribution of available hydrogen in the refinery, using additional hydrogen production system and by importing. Amongst these options first one is cost effective as no addition source is required. Thus, the present paper deals with optimum distribution of hydrogen. For this purpose mathematical models are developed based on pressure constraints, source and sinks constraints, compressor flow rate recycle and purity constraints, flow combinations, hydrogen consumption, operating cost, capital cost, payback period etc. The model is developed in stages to show the improvements.Two case studies, Cases A and B, are considered in this paper and for these linear programming (LP), nonlinear programming (NLP), mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP) models are developed. The results predicted using these models are compared to select the best network. The optimized network for Case A consumes 20.9% less hydrogen, whereas, for Case B it is reduced by 32.3%.     

基于大M法的MILP的风电区间可优化和可调节的安全约束机组组合

不同于现有的鲁棒机组组合,本章提出了基于大M法的MILP的风电区间可优化和可调节的安全约束机组组合。含大规模风电接入的电网,其所能消纳的风电出力区间受到电网备用容量和线路传输功率两方面约束。本文提出设置最优弃风限制以确定安全风电出力区间,保证电网备用容量充裕且各条线路的传输功率不越限。在算法上,基于大M法将初始风电出力...     

Risk-averse based optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel hybrid energy system in the electricity/hydrogen markets

The techno-economic advantages of grid-connected hybrid energy system (HES) exploit synergies to improve reliability and economic efficiency while maintaining grid stability. Therefore, this paper proposes a risk-averse optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel HES to participate into two energy markets including electricity and hydrogen markets. The grid company can flexibly trade power into two markets to maximally achieve profits based on price arbitrage. The risk influences of the uncertainties, i.e., photovoltaic/wind generation, and electricity prices on the expected revenue are evaluated with CVaR model. For a better exhibition of seasonal variability effects on HES optimal operation strategy, two typical Spring/Summer days are chosen. The proposed risk-averse optimal operational strategy is formulated as a two-stage mixed-integer linear programming (MILP) model. The results in a Spring day simulation under non-risk situation indicate that the overall expected revenue can be improved 2.74 times larger if considering hydrogen market. Moreover, the optimal operational strategy of hydrogen production is considerably affected by unpredictable wind farm. Sensitivity analysis also validates that the changes of PV/WT curtailment penalty have a profound influence than battery degradation coefficient on the HES expected revenue.     

Short term scheduling of multiple grid-parallel PEM fuel cells for microgrid applications

This paper presents a short term scheduling scheme for multiple grid-parallel PEM fuel cell power plants (FCPPs) connected to supply electrical and thermal energy to a microgrid community. As in the case of regular power plants, short term scheduling of FCPP is also a cost-based optimization problem that includes the cost of operation, thermal power recovery, and the power trade with the local utility grid. Due to the ability of the microgrid community to trade power with the local grid, the power balance constraint is not applicable, other constraints like the real power operating limits of the FCPP, and minimum up and down time are therefore used. To solve the short term scheduling problem of the FCPPs, a hybrid technique based on evolutionary programming (EP) and hill climbing technique (HC) is used. The EP is used to estimate the optimal schedule and the output power from each FCPP. The HC technique is used to monitor the feasibility of the solution during the search process. The short term scheduling problem is used to estimate the schedule and the electrical and thermal power output of five FCPPs supplying a maximum power of 300 kW.     

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.     

Risk-constrained optimal operation of fuel cell/photovoltaic/battery/grid hybrid energy system using downside risk constraints method

Residential consumers have electrical and thermal loads. Therefore they can be utilized hybrid thermal and electrical energy systems to procure their required energy. In the proposed system, in order to supply residential loads, a hybrid energy system (HES) is proposed which consists of photovoltaic/solid oxide fuel cell/thermal and electrical storages/boiler. Also, the uncertain parameters such as thermal and electrical loads, electricity market price, and solar irradiation are considered in the stochastic formulation. Uncertain parameters can be led to financial risks in the system operation. In order to measure imposed risks, in this paper, a novel risk management method called downside risk constraints method is used to model the financial risks imposed from the uncertain parameters. According to obtained results, the operator of the hybrid energy system by utilization of the downside risk constraints method has obtained a strategy that is scenario independent. In other words, the downside risk constraints method by minimizing the imposed risks introduced a zero-risk strategy which operation cost would not increase by changing the scenario. Results are shown that system operators by paying 1.3% more expected cost ($ 40.22 instead of $ 39.69), can make its operation independent of the scenario. Also, risk-based operational strategies of the proposed hybrid energy system are reported in the results as graphical results. The proposed risk-measurement operation problem of the designed hybrid energy system is formulated as a mixed-integer linear programming (MILP) model and modeled by GAMS software using CPLEX solver.     

Advanced optimal planning for microgrid technologies including hydrogen and mobility at a real microgrid testbed

This paper investigates the optimal planning of microgrids including the hydrogen energy system through mixed-integer linear programming model. A real case study is analyzed by extending the only microgrid lab facility in Austria. The case study considers the hydrogen production via electrolysis, seasonal storage and fueling station for meeting the hydrogen fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative to two different reference cases which satisfy the mobility demand by diesel fuel and utility electricity based hydrogen fuel production respectively. The key results indicate that the low emission hydrogen mobility framework is achieved by high share of renewable energy sources and seasonal hydrogen storage in the microgrid. The investment optimization scenarios provide at least 66% and at most 99% carbon emission savings at increased costs of 30% and 100% respectively relative to the costs of the diesel reference case (current situation).     

Chance-constrained energy-reserve co-optimization scheduling of wind-photovoltaic-hydrogen integrated energy systems

An optimal energy-reserve scheduling model of wind-photovoltaic-hydrogen integrated energy systems (WPH-IES) with multi-type energy storage devices including electric, thermal and hydrogen is presented in this paper. The chance-constrained programming (CCP) theory is utilized to model the impact of renewable and load uncertainties on reserve constraints. Considering the non-convex of proposed CCP model, an improved discretized step transformation (DST) method is proposed to transform the CCP problem into a solvable mixed integer linear programming (MILP) formulation. In addition, a critical threshold value selection approach is developed to reduce the number of constraints and improve solution efficiency. Case studies demonstrate that the proposed energy-reserve model can reduce the total operating cost on the premise of ensuring the safety of system operation. The combined improved DST and critical threshold value selection method can reduce computational burden and improve the accuracy of scheduling results.     

Risk-constrained scheduling of a CHP-based microgrid including hydrogen energy storage using robust optimization approach

Recently, the integration of various energy resources, including renewable generation and combined heat and power (CHP) units in microgrids, has created the opportunity of off-grid operation with a suitable range of reliability. This paper presents an optimization model to schedule an islanded MG with various resources, including CHP, photovoltaic (PV), and boiler, as the primary energy provision sources besides electric battery storage, thermal storage and hydrogen energy system (HES). The HES has the power-to-hydrogen (P2H) and hydrogen-to-power (H2P) modes, which increases the flexibility of the scheduling. The uncertainty management is the most essential task in the CHP-based MGs scheduling problem, since the power and heat productions are interrelated and can result in economic losses without enough deliberations. Hence, this paper proposes the robust optimization approach (ROA) to cope with the uncertainties associated with the PV production and electric and heat load demands. The robust counterparts are applied to the deterministic problem to create a tractable adjustable robust framework. The problem is structured as a mixed-integer linear programming (MILP) handled by the General Algebraic Modeling System (GAMS) using CPLEX solver. The results verified the effectiveness of the proposed robust counterparts in managing the associated risk. The results illustrated a conscious scheduling strategy under robust conditions. However, the more preserved decisions are taken, the higher operational cost is realized. In this regard, the increment of robustness level from the lowest value (deterministic condition) to the highest value (conservatism condition) increased the operation cost by about 43.29%.     

Hydro energy systems management in Portugal: Profit-based evaluation of a mixed-integer nonlinear approach

In this paper, a novel mixed-integer nonlinear approach is proposed to solve the short-term hydro scheduling problem in the day-ahead electricity market, considering not only head-dependency, but also start/stop of units, discontinuous operating regions and discharge ramping constraints. Results from a case study based on one of the main Portuguese cascaded hydro energy systems are presented, showing that the proposed mixed-integer nonlinear approach is proficient. Conclusions are duly drawn.     

基于改进型运行策略的独立微电网容量优化配置

建立独立微电网对于解决离岸岛屿等传统供电不便地区的可靠自主供用电问题具有非常显著的实用性。针对风/光/蓄/柴独立微电网,提出了一种考虑系统净负荷大小的改进型运行控制策略,通过权重法将系统经济性和环保性指标整合并加入可靠性经济惩罚共同作为优化目标,考虑多种约束条件,建立基于改进型运行策略的风/光/蓄/柴独立微电网容量优化配置模型,并采用混合量子遗传算法(HQGA)对我国沿海某岛地区独立微网实施优化配置。结果表明,与采用常规运行策略的配置结果相比,所提运行策略具有正确性和优越性。     

基于氢气储能的热电联供微电网容量优化配置

以光伏系统、氢燃料电池、电解槽、储氢罐构建的热电联供微电网为研究对象,制定初始投资成本等年值以及年运行成本最小的优化目标,提出热电联供微电网热负荷满足率评价指标,针对系统运行的基本约束设计微电网控制综合策略,并以某地历史源荷数据为参考,建立满足工程应用的数学模型,采用粒子群优化算法进行求解,得到氢气储能的孤岛型微电网热电需求基本方案。从应用层面论证氢气储能替代电池储能的可行性,并进行微电网系统容量优化配置,可满足居民负荷供能需求,提高系统运行经济性,预期具有较好的应用前景。     

Optimal hydro scheduling and offering strategies considering price uncertainty and risk management

Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system.     

A comparison study of battery size optimization and an energy management strategy for FCHEVs based on dynamic programming and convex programming

Aiming to address the hydrogen economy and system efficiency of a fuel cell hybrid electric vehicle, this paper proposes comparison research of battery size optimization and an energy management strategy. One approach is based on a bi-loop dynamic programming strategy, which selects the optimal one by initializing the battery parameters in the outer loop and performs energy distribution in the inner loop. The other approach is a framework based on convex programming, which can simultaneously design energy management strategies and optimize battery size. In the dynamic programming algorithm, the influence of the different discrete steps of state variables on the results is analysed, and a discrete step that can guarantee the accuracy of the algorithm and reduce computational time is selected. The results based on the above two algorithms and considering the transient response limitations of the fuel cell are analysed as well. Finally, two driving cycles are chosen to verify and compare the performance of the proposed methodology. Simulation results show that the dynamic programming-based energy management strategy and battery size provide more accurate results, and the transient response of the fuel cell has little effect on the optimization results of the battery size and energy management strategies.     

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。     

Distributionally robust optimization of home energy management system based on receding horizon optimization

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.     

Economic droop parameter selection for autonomous microgrids including wind turbines

Droop control is a key strategy for operating islanded microgrid systems. The droop settings of the different distributed generation (DG) units in an islanded microgrid determine the operational characteristics of the island. This paper presents an algorithm for choosing the optimal droop parameters for islanded microgrids with wind generation in order to minimize the overall island generation costs in the absence of a microgrid central controller (MGCC). A detailed microgrid model is adopted to reflect the special features and operational characteristics of droop controlled islanded microgrid systems. The proposed problem formulation considers the power flow constraints, voltage and frequency regulation constraints, line capacity constraints and unit capacity constraints. Numerical case studies have been carried out to show the effectiveness of the proposed algorithm as compared to conventional droop parameter selection criteria typically adopted in the literature.