Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects

Along with the continuing global warming, the environmental constraints are expected to play more and more important role in the operation of distributed energy resource (DER) systems, besides the economic objective. In this study, a multi-objective optimization model is developed to analyze the optimal operating strategy of a DER system while combining the minimization of energy cost with the minimization of environmental impact which is assessed in terms of CO₂ emissions. The trade-off curve is obtained by using the compromise programming method. As an illustrative example, the DER system installed in an eco-campus in Japan has been selected for case study. The distributed technologies under consideration include photovoltaics (PV), fuel cell and gas engine for providing electrical and thermal demands. The obtained results demonstrate that increasing the satisfaction degree of economic objective leads to increased CO₂ emissions. The operation of the DER system is more sensitive when environmental objective is paid more attention. Moreover, according to the sensitivity analysis, the consideration of electricity buy-back, carbon tax, as well as fuel switching to biogas, has more or less effect on the operation of DER systems.     

Modeling and operation optimization of RE integrated microgrids considering economic,energy, and environmental aspects

Microgrids provide promising solution for integration of renewable energy sources in the electrical grid. To exploit the key benefits, achieving the economical operation of renewable aided microgrids has become necessary and is a challenging task. This paper presents an efficient optimization model to minimize the operational cost of a solar integrated microgrid. We formulate a joint optimization mixed integer problem for appropriate modeling of the system under various practical constraints. An efficient solution is obtained with a distributed approach such that the original problem is solved in two stages. Dual decomposition approach is adopted for cost, emissions, and solar share optimization. Lagrange relaxation, Lambda iteration method, and binary integer programming are employed to obtain the joint optimization solution. Finally, the performance of the proposed model is validated through simulations that show that an overall cost reduction of 4.2070e+04 $ and emission reduction of 7.2001e+03 kg are achieved with the proposed model.     

楼宇型分布式能源系统设备容量和运行策略优化研究

设备容量优化和运行策略优化是分布式能源系统设计,运行的关键问题。为实现分布式能源系统的经济效益,能效水平和环境效益最大化,针对楼宇型分布式能源系统建立了相对普适化的物理模型和数学模型,以粒子群优化算法和线性规划相结合,采用两阶段优化方法计算系统的最优容量配置,并给出运行策略。以某写字楼的分布式能源系统为例,得到最优的系统设备容量和全年逐时运行策略,并采用遍历法验证计算结果的准确性。优化的分布式能源系统与传统供能系统相比,费用年值降低7.79%,年总能耗降低24.18%,污染物排放量减少了62.77 %。     

Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

In this paper, a multi-objective optimization model is established for the investment plan and operation management of a hybrid distributed energy system. Considering both economic and environmental benefits, the overall annual cost and emissions of CO₂ equivalents are selected as the objective functions to be minimized. In addition, relevant constraints are included to guarantee that the optimized system is reliable to satisfy the energy demands. To solve the optimization model, the nondominated sorting generic algorithm II (NSGA-II) is employed to derive a set of non-dominated Pareto solutions. The diversity of Pareto solutions is conserved by a crowding distance operator, and the best compromised Pareto solution is determined based on the fuzzy set theory. As an illustrative example, a hotel building is selected for study to verify the effectiveness of the optimization model and the solving algorithm. The results obtained from the numerical study indicate that the NSGA-II results in more diversified Pareto solutions and the fuzzy set theory picks out a better combination of device capacities with reasonable operating strategies.     

Sustainable energy system design with distributed renewable resources considering economic,environmental and uncertainty aspects

Electricity generation using renewable energy generation technologies is one of the most practical alternatives for network planners in order to achieve national and international Greenhouse Gas (GHG) emission reduction targets. Renewable Distributed Generation (DG) based Hybrid Energy System (HES) is a sustainable solution for serving electricity demand with reduced GHG emissions. A multi-objective optimisation technique for minimising cost, GHG emissions and generation uncertainty has been proposed in this paper to design HES for sustainable power generation and distribution system planning while considering economic and environmental issues and uncertainty in power availability of renewable resources. Life cycle assessment has been carried out to estimate the global warming potential of the embodied GHG emissions from the electricity generation technologies. The uncertainty in the availability of renewable resources is modelled using the method of moments. A design procedure for building sustainable HES has been presented and the sensitivity analysis is conducted for determining the optimal solution set.     

含可响应分布式电源的智慧能源配网多目标优化调度

分布式电源增加了配电网的灵活性,但同时也给配网调度带来一定挑战。文章针对含有可响应分布式电源的配网多目标优化调度进行研究。首先分析了居民智慧能源的特征以及居民用户、分布式电源、运行商三者的关系;然后考虑了分布式电源的不确定性,建立了风机和光伏的出力模型,从经济性、环保和配网可靠性角度建立了居民智慧能源配网多目标调度优化模型;针对配网负荷多样化的特点,提出了负荷因数模型,计及到多目标优化模型中;最后利用决策算法求解该模型,并在IEEE33节点系统中进行仿真,说明了所提模型和算法的有效性。     

一种新型分布式供能系统的研究

本文在介绍了生物质能的利用方式以及分布式供能系统的基础上,介绍了一种以生物质能为原料的可再生能源的分布式供能方式,详细介绍了系统中各设备的运行情况,并分析了这种分布式供能系统的经济效益和社会效益。     

Techno‐economic optimization of hybrid power system using genetic algorithm

This paper presents an optimum sizing methodology to optimize the hybrid energy system (HES) configuration based on genetic algorithm. The proposed optimization model has been applied to evaluate the techno‐economic prospective of the HES to meet the load demand of a remote village in the northern part of Saudi Arabia. The optimum configuration is not achieved only by selecting the combination with the lowest cost but also by finding a suitable renewable energy fraction that satisfies load demand requirements with zero rejected loads. Moreover, the economic, technical and environmental characteristics of nine different HES configurations were investigated and weighed against their performance. The simulation results indicated that the optimum wind turbine (WT) selection is not affected only by the WT speed parameters or by the WT rated power but also by the desired renewable energy fraction. It was found that the rated speed of the WT has a significant effect on optimum WT selection, whereas the WT rated power has no consistent effect on optimal WT selection. Moreover, the results clearly indicated that the HES consisting of photovoltaics (PV), WT, battery bank (Batt) and diesel generator (DG) has superiority over all the nine systems studied here in terms of economical and environmental performance. The PV/Batt/DG hybrid system is only feasible when wind resource is very limited and solar energy density is high. On the other hand, the WT/Batt/DG hybrid system is only feasible at high wind speed and low solar energy density. It was also found that the inclusion of batteries reduced the required DG and hence reduced fuel consumption and operating and maintenance cost. Copyright © 2014 John Wiley & Sons, Ltd.     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.     

考虑热惯性的居民楼宇综合能源系统日前运行优化

针对居民楼宇中部分热负荷的热惯性特征,提出了一种考虑热惯性的居民楼宇综合能源系统日前运行优化方法。首先,建立了居民楼宇综合能源系统的典型结构和数学模型,探究能源供给、转换、分配、存储的机理;其次,基于线性能量平衡法,采用微分方程描述楼宇制冷采暖系统和生活热水系统的热惯性特征,并将上述方程差分化;再次,以综合成本最小为目标,考虑购能约束、功率平衡约束、热惯性约束、备用容量约束等,建立日前运行优化模型,并将上述模型转化为混合整数二次规划,通过Yalmip调用Cplex求解;最后,通过实际算例分析热惯性对居民楼宇综合能源系统运行优化的影响。结果表明,利用热惯性能够对冷热负荷进行平移或削减,从而降低运行成本。     

考虑电转氢气过程及综合需求响应的电-氢-气综合能源系统协调优化运行

文章介绍了电转氢气、氢气转天然气的过程,并在此基础上构建了电-氢-气综合能源系统。通过考虑综合需求侧响应,建立以最小化系统运行总成本为目标函数的协调优化模型,利用禁忌搜索算法和粒子群算法相结合的改进算法进行求解。经验证,考虑电转氢气过程及综合需求响应的协调优化方案增强了综合能源系统的经济性、环保性,提高了新能源的消纳能力。     

Synthesis,design and operation optimization of a marine energy system

Recent developments in the global fuel markets imposed the need of increased fuel economy and cost effectiveness of sea-going vessels. Optimization of the ship's total energy system, as a whole, is now a demand of the marine industry sector in order to address the significant increase of installation and operational costs. This study is focused on the synthesis, design and operation optimization of a marine energy system. A realistic example of a cruise liner energy system has been selected. Basic technology options have been identified and a generic energy system model has been constructed. Various configuration options, types of technologies and existence of components have been incorporated in the generic system. In addition, time varying operational requirements for this cruise liner ship have been considered, resulting in a time dependent operation optimization problem. The complete optimization problem has been solved using a novel algorithm, inspired by evolutionary and social behavior metaphors. A parametric analysis with respect to the fuel price demonstrated changes in the optimum synthesis of the system.     

基于改进萤火虫算法的分布式能源供应链配置研究

针对分布式能源供应链的配置问题,提出了改进人工萤火虫算法,结合云计算技术解决该配置问题。首先,以人工萤火虫算法的决策域半径为切入点,改进人工萤火虫算法的决策域半径,有效地解决了传统人工萤火虫算法寻优不稳定、算法精度低、后期收敛速度较慢的缺点;其次,全面采集系统信息,考虑各云处理中心各服务器的负载情况,建立基于改进人工萤火虫算法的分布式能源供应链配置需求侧均衡模型,以达到云计算环境下能源供应链中的配置均衡目标;最后,仿真分析表明,改进人工萤火虫算法可以更快、更稳定、更均衡地处理系统中的任务,优化分布式能源供应链配置。     

Modeling of integrated renewable energy system for electrification of a remote area in India

Over the years, renewable energy based power generation has proven to be a cost-effective solution in stand-alone applications in the regions where grid extension is difficult. The present study focused on the development of models for optimal sizing of integrated renewable energy (IRE) system to satisfy the energy needs in different load sectors of four different zones considered in Chamarajanagar district of Karnataka state in India. The objective of the study is to minimize the total cost of generation and cost of energy using genetic algorithm (GA) based approach. Considering optimization power factor (OPF) and expected energy not supplied (EENS), optimum system feasibility has been investigated. Based on the study, it has been found that IRES is able to provide a feasible solution between 1.0 and 0.8 OPF values. However, power deficit occurs at OPF values less than 0.8 and the proposed model becomes infeasible under such conditions. Customer interruption cost (CIC) and deficit energy (DE) for all zones were also computed to quantify the reliability of the systems.     

能源供需优化模型在北京市能源系统的应用

依据北京市“十一五”时期发展规划，立足于北京市目前能源消费现状，创建了能源供需优化模型，旨在满足能源需求、环境允许的前提下使得系统能源费用最小化。采用了区间线性优化方法，对已知上下界但其分布未知的参数用区间数表示，最后得出规划期的三个周期内八种能源的产出调入量以及各个周期的总费用，并对目前北京市能源消费中存在的问题提出了几点建议。     

太阳能冷热电联供分布式能源系统的研究

以太阳能应用为背景，讨论了能够实现独立建筑冷热电联供的两种分布式能源系统的原理。以太阳能作为唯一热源，用于加热气体工质，进行闭式Brdyton循环发电。其透平释放的余热通过余热制冷方式供冷或通过换热器直接供热．可实现独立建筑的冷热电联供。当把燃料电池系统和该热动力系统组合起来，则可实现白天和夜间连续的独立建筑冷热电联供。该系统不消耗化石能源，无污染，能源利用效率高，具有进一步理论研究的价值和推广应用潜力。     

分布式电源并网系统的研究

随着目前电力短缺的加剧,分布式电源与区域电网并联运行的趋势越来越明显.在分布式发电装置与电网并联运行的模式下,通过分析逆变型分布式电源＂负荷＂特性,提出基于功率电流双环控制策略的逆变控制系统.通过合理选取功率控制环、电流控制环、软件锁相环的PI控制器参数,可以使得分布式发电并网逆变装置具有良好的稳态及动态性能.通过在MATLAB上实现的仿真模型证明该控制系统能较好地维持逆变型分布式电源恒功率电压源的拟负荷外特性,控制灵活,反应迅速.     

Cross-regional integrated energy system scheduling optimization model considering conditional value at risk

Integrated energy system is a very important way to improve energy efficiency. Based on the combined heating cooling and power system, combined with energy storage equipment, a cross-regional integrated energy system scheduling optimization problem is studied. An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region. Meanwhile, the value at risk (VaR) theory is introduced and the operating constraints of equipment in the integrated energy system fully considered, the integrated energy system scheduling model with VaR is established. The example shows that the model can realize multi-type electrical, heating, and cooling load optimized by schedule across regions under the premise of satisfying the balance of energy supply and demand, which can reduce the system operation cost. The sensitivity analysis of the minimum expected cost and the influencing factors of conditional VaR is carried out to verify the validity and feasibility of the proposed model.

• An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region.
• By using the conditional value at risk theory to consider various types of the integrated energy system complements and evaluates the operational risk of the system under optimal operating conditions of the system.
• The total cost of system scheduling operation is proportional to the storage capacity, which is inversely proportional to the heat storage capacity and inversely proportional to the pipeline capacity within a certain interval.

     

Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (E_excess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.     

Multi-objective optimized operation of integrated energy system with hydrogen storage

In this paper, an integrated energy system (IES) consisting of wind turbine unit, photovoltaic cell unit, electrolytic hydrogen unit, fuel cell unit, and hydrogen storage unit is proposed, and the construction of multi objectives for day-ahead power dispatching of the IES considering both operation and environment cost is discussed. By adopting piecewise linearization method, the optimization variables are divided into 24 periods, and the day-ahead power dispatching optimization problem is transformed into a 24-h piecewise optimization problem. On the basis, a complete non-linear mixed integer dynamic scheduling optimization model is established. An improved non-dominated sorting genetic algorithm (NSGA-II) is applied to solving the model. In optimization process, an interactive strategy is adopted to solve the coordination between discretization of variables and restriction of switching times of electrolyzer. Optimization results show that, compared with the single objective of minimizing operating costs, the multi-objective optimization scheme can reduce carbon emissions by 3.5% with 2.8% increase of operating cost. Compared with the single objective of minimizing environmental, the multi-objective optimization scheme can reduce operating cost carbon by 5.12% with 2.6% increase of environmental cost.