能源互联网共享运营平台关键技术及应用

能源互联网是充分利用云大物移智链等先进IT技术,整合数据资源,贯通产业链上下游,实现能源电力全过程的精益管理、高效运行、共享服务。文章从能源互联网对外共享和运营的业务痛点出发,详细设计了运营服务平台的业务架构、应用架构、技术架构和数据架构,并研究了共享运营平台的多源异构数据处理与存储、海量异构数据统一计算、敏捷开发和自动化部署等关键技术,通过整合营销、调度、配电等内部数据以及经济、环境、政策等外部数据,支撑用电数据的深度挖掘和增值服务,最后设计了面向工业用户的典型应用场景。     

考虑需求响应和V2G的低碳能源网控制策略

为平抑大量电动汽车(EV)入网所致的负荷波动,实现能源网低碳电力运行,通过结合V2G和P2G技术,将电力系统与天然气系统之间的能量耦合。先引入需求响应策略建立负荷侧分时电价,引导大规模EV参与V2G系统,改变用户的用能时段;其次考虑碳排放环境因素建立低碳能源网模型,以经济性最优为目标利用禁忌-粒子群算法进行求解;最后利用算例对比分析了低碳能源网的能量调度和运行成本情况,验证了所提方法的有效性。     

Analysis of access mode and control strategy of distributed energy storage system on user side

分布式储能系统在电力用户侧中的应用日益广泛,且应用场景多样化,对于电网来说是潜在的优良资源。然而其容量小、数量多、分布不均衡、单机接入成本高、系统操作及管理困难,给电网的规划运营带来了日益严峻的挑战和技术难题。本文就用户侧中的分布式储能典型应用模式及接入方式进行介绍,阐述各应用场景下分布式储能的应用模式和方案,并分析了典型应用场景下的分布式储能运行效果。通过对分布式储能系统技术形态和载体的分析,可为进一步研究分布式储能汇聚协调控制技术提供理论指导。     

基于双层合作博弈的虚拟电厂滚动优化调度研究

为了应对当下高比例可再生能源和大规模电动汽车的接入给虚拟电厂双侧运行带来的挑战，提出了一种改进的Shapley值分配方法用于解决联盟收益分配问题。以虚拟电厂为中介，分别构建了以联盟收益最大化为目标的“需”侧用户-虚拟电厂联盟和“供”侧虚拟电厂-主动配电网联盟，再将双侧的合作博弈模型联系起来，构成基于双层合作博弈的虚拟电厂滚动优化调度模型。利用改进的Shapley值进行联盟收益再分配，从而达到用户、虚拟电厂和主动配电网三方主体收益的最大化，最后进行实验验证。结果表明：该模型使三方主体收益相较于未合作博弈和任一单层合作博弈分别提高了15.64%，6.95%和6.949%，提高了系统运行的经济性。     

联网光伏电站可调度运行特性分析

由于受光照、温度等自然条件的影响,目前光伏电站联网运行时不具备可调度性,给电网稳定运行带来一定影响,造成电力用户供电电能质量不高。分析了配备混合储能系统的联网光伏电站的基本结构,从电网角度探讨了光伏电站的可调度运行模式;针对光伏电源输出功率波动问题,采用混合储能对其进行平滑控制,优化了输出功率特性,为制定调度计划奠定了基础。通过Matlab编程仿真,验证了所提出方法的有效性 。     

基于工业互联网的船用燃气轮机健康管理平台

船用燃气轮机由于结构复杂、工作环境恶劣,对其进行状态监测与健康管理十分必要。通过工业互联网技术对燃气轮机运行过程中产生的大量数据进行分析,可以极大提升处理效率。本文调研了现有健康管理信息系统应用情况,通过案例分析说明了国内外先进的工业互联网平台的结构组成和主要功能,基于工业互联网面向船用燃气轮机提出了健康管理平台总体架构,并对其各个层次进行分析。该平台架构将工业互联网的优势与船用燃气轮机健康管理的实际应用需求结合,实现状态监测、故障分析、寿命预测等重要功能,为用户提供决策支持。     

Decoupling optimization of integrated energy system based on energy quality character

Connections among multi-energy systems become increasingly closer with the extensive application of various energy equipment such as gas-fired power plants and electricity-driven gas compressor. Therefore, the integrated energy system has attracted much attention. This paper establishes a gas-electricity joint operation model, proposes a system evaluation index based on the energy quality character after considering the grade difference of the energy loss of the subsystem, and finds an optimal scheduling method for integrated energy systems. Besides, according to the typical load characteristics of commercial and residential users, the optimal scheduling analysis is applied to the integrated energy system composed of an IEEE 39 nodes power system and a 10 nodes natural gas system. The results prove the feasibility and effectiveness of the proposed method.     

Modeling alkaline water electrolysis for power-to-x applications: A scheduling approach

The flexible operation of alkaline water electrolyzers enables power-to-x plants to react efficiently to different energy scenarios. In this work, a novel scheduling model for alkaline water electrolysis is formulated as a mixed-integer linear program. The model is constructed by implementing operational states (production, standby, idle) and transitions (cold/full startup, shutdown) as integer variables, while the power loading and hydrogen flowrate are set as continuous variables. The operational characteristics (load range, startup time, ramp rates) are included as model constraints. The proposed model allows finding optimal number of electrolyzers and production schedules when dealing with large data sets of intermittent energy and electricity price. The optimal solution of the case study shows a balance between hydrogen production, energy absorption, and operation and investment costs. The optimal number of electrolyzers to be installed corresponds to 54% of the ones required to absorb the highest energy peak, being capable of loading 89.7% of the available energy during the year of operation, with an overall plant utilization of 93.7% and 764 startup/shutdown cycles evenly distributed among the units.     

Modelling renewable supply chain for electricity generation with forest, fossil, and wood-waste fuels

In this paper, a multiple objective model to large-scale and long-term industrial energy supply chain scheduling problems is considered. The problems include the allocation of a number of fossil, peat, and wood-waste fuel procurement chains to an energy plant during different periods. This decision environment is further complicated by sequence-dependent procurement chains for forest fuels. A dynamic linear programming model can be efficiently used for modelling energy flows in fuel procurement planning. However, due to the complex nature of the problem, the resulting model cannot be directly used to solve the combined heat and electricity production problem in a manner that is relevant to the energy industry. Therefore, this approach was used with a multiple objective programming model to better describe the combinatorial complexity of the scheduling task. The properties of this methodology are discussed and four examples of how the model works based on real-world data and optional peat fuel tax, feed-in tariff of electricity and energy efficiency constraints are presented. The energy industry as a whole is subject to policy decisions regarding renewable energy production and energy efficiency regulation. These decisions should be made on the basis of comprehensive techno-economic analysis using local energy supply chain models.     

考虑广义储能的综合能源系统典型日优化运行

由电、热、冷能系统之间的耦合与互联构成的综合能源系统(IES),对于构建环保、高效、经济的能源系统至关重要。着眼于IES运行环节,考虑广义储能设备的投入运行,并计及IES内各设备出力约束,以调度周期内运行费用最低为目标函数,采用基于yalmip平台的cplex优化算法包对IES优化运行进行求解。该方法考虑了分时电价和典型日负荷特性对IES运行的影响,并与不计广义储能的运行模式进行对比分析。算例仿真表明,投入广义储能设备不仅更有利于IES的经济效益,而且有利于IES安全稳定运行。     

Master‐slave game optimization method of smart energy systems considering the uncertainty of renewable energy

As the uncertainty of renewable energy output brings more and more risks to the day‐ahead dispatch of the power grid, an optimization scheduling strategy of a smart energy system based on improved master‐slave game model is proposed. Risk factors related to the uncertainties of renewable energy are introduced into the master‐slave game model. Taking the smart energy system as the leader and the end users as the follower, an optimized operation model of the smart energy system based on the improved master‐slave game model is established, which is transformed into a single‐layer linear programming model according to the Karush‐Kuhn‐Tucher conditions and the duality theorem. The benefits of the system and electric vehicle users in four application scenarios are obtained by the YALMIP algorithm and the sensitivity affecting the economics of the smart energy system is analyzed. The validity of the model is verified by a simulation analysis of actual operation data from the smart energy system in China. The simulation results show that the method proposed in this paper can increase the revenue of the smart energy system by 7%, reduce the risk cost and charging cost of electric vehicle users by 63.92% and 48.34%.     

Priority and delay constrained demand side management in real‐time price environment with renewable energy source

In smart grid, integration of renewable energy sources such as solar and wind is a challenging task because of their intermittent nature. Most of the existing demand side management techniques are based on day‐ahead pricing or time of use pricing that deviate from real‐time pricing because of unpredictable energy consumption trends and electricity prices. This paper presents opportunistic scheduling algorithms in a real‐time pricing environment based on optimal stopping rule. We classify different users and assign priorities based on energy demand. In order to minimize the electricity bill and appliance waiting time cost, we modify the first come first serve scheduling algorithm. Regarding comfort maximization, priority enable early deadline first scheduling algorithm is proposed, which schedules the appliances based on minimum length of operation time and priority constraints. Simulation results validate the effectiveness of the proposed algorithms in terms of electricity cost reduction and user comfort maximization. Copyright © 2016 John Wiley & Sons, Ltd.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

Modelling and analysis of resource scheduling in restructured power systems considering wind energy uncertainty

Concerns over rapid transformation of global climate patterns resulted in wide deployment of renewable energy sources especially wind energy. However, intermittent nature of wind energy makes resource scheduling aspect of system operator more complex. The emerging deregulation policies have added to the complexity of scheduling problem. Therefore, treating and scheduling of wind energy on deterministic basis would result in non-optimal energy dispatch and increased operation cost over scheduling horizon. This paper focuses on optimal scheduling methodology of wind energy in day ahead market (DAM) considering expected costs due to wind uncertainty in real time (RT) spot market under deregulated market structure. This paper introduces various possible costs, namely, spot market compensation cost, additional reserve cost and rescheduling cost applicable to deviation in scheduled power from DAM to spot market. The costs can be derived from front-end information of DAM, historical and statistical estimate of RT market conditions. The effectiveness of proposed methodology is examined using IEEE 30 bus system with thermal and wind energy generators. In addition, sensitivity analysis is performed to examine the effect of incentive margin on DAM scheduling. Simulation results are presented, discussed and affirmed the effectiveness of proposed scheduling methodology.     

考虑电转气消纳风电的电-气综合能源系统两阶段鲁棒协同调度

针对传统随机规划方法和区间优化方法处理风电出力不确定性的不足之处,该文提出含电转气设备的电力-天然气综合能源系统两阶段鲁棒协同调度模型,并考虑天然气网络运行约束对燃气轮机和电转气设备调度出力及备用配置的影响。模型以风电基准场景下系统的日前调度运行成本及最劣风电场景下实时调度成本之和为目标函数,建立具max-min结构的双层优化模型,并在主/子问题求解框架下采用列约束生成（C&CG）方法进行求解。最后,在Matlab平台下构建仿真算例验证所提鲁棒协同调度模型的有效性。     

Energy station and distribution network collaborative planning of integrated energy system based on operation optimization and demand response

In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative optimization are proposed considering multiagent interest balance. First, the three-stage optimization framework and model of station and network planning of IES are proposed based on the operation scheduling of IES and the response of energy station and users as different subjects, aiming at optimization of station and network cost-effectiveness, respectively. Second, the distribution network simplification method is proposed based on topological equivalence principle. The topological structure characteristics of distribution network is analyzed, the rapid generation strategy of single tie line network based on broken circle method is proposed, and then an encoding/decoding scheme is proposed based on particle swarm optimization algorithm. Third, considering the load characteristics and energy station demand response based on flexible comfort level of users, a dynamic spot price optimization method and operation strategy of IES are proposed. And then the station and network collaborative planning solution is presented based on the operation optimization of IES. Finally, an example is given to verify the practicability and effectiveness of the proposed method in this paper.     

计及火用分析的综合能源系统多目标优化调度

建立综合能源系统优化调度模型并进行高效求解有利于可再生能源的开发利用,发掘综合能源系统降本增效的潜力。针对含光伏发电的综合能源系统,以系统火用效率倒数最小和总运行成本最小为目标,结合电-热-冷综合需求响应模型和运行约束,构建综合能源系统多目标运行模型。针对模型中存在的非凸非线性项进行等价线性转化处理,将问题由多目标混合整数线性分式规划等价转换为多目标混合整数线性规划,进一步采用ε约束法将其转换为一系列单目标混合整数线性规划问题进行高效求解获得帕累托Pareto前沿,并采用TOPSIS法进行决策。算例仿真表明,所建立的含光伏发电的综合能源系统能提升系统运行灵活性,相比于单目标运行,计及火用分析的综合能源系统多目标优化调度能够实现系统运行成本和火用效率的折衷。     

Social,cultural and political dimensions of off-grid renewable energy programs in developing countries

Renewable energy based off-grid rural electrification programs are one of the most effective ways to increase access to energy in remote areas of developing countries. While a large number of such programs have been implemented, the rate of success, to ensure a long-term sustainable program, is low. Many of these programs have failed to appropriately address the social and cultural issues of target communities, which resulted low or no acceptance by the users. Similarly, many rural electrification policies fail to incorporate the needs and views of the users resulting in an unresponsive policy measures. In this paper a framework is presented that would assist in assessing community attitudes and their needs by presenting key factors that need to be considered in different stages of program development and implementation. The framework has been developed in line with the concept of “diffusion of innovation”, which has been found to be in agreement with the stages of off-grid renewable energy program development. It is anticipated that this holistic framework will not only help to achieve a long-term sustainable program, but will also offer a significant contribution in achieving Sustainable Development Goals (SDGs) by improving access to energy for rural people in developing countries.     

基于风-光-蓄-火联合发电系统的多目标优化调度

针对风电、光伏出力的随机性、间歇性和波动性而导致其在大规模接入电网时对电网发电计划制定和调度产生的影响,提出了含风-光-蓄-火联合发电系统的多目标优化调度模型。利用抽水蓄能的抽蓄特性,将风电和光伏出力进行时空平移,使风-光-蓄联合出力转变为稳定可调度电源,具备削峰填谷的功能,与火电机组共同参与系统优化调度。以风-光-蓄联合出力最大、广义负荷波动最小和火电机组运行成本最小作为目标函数,建立多目标优化调度模型,通过多目标处理策略,使目标函数简化为2个,以降低问题维数;在求解阶段,利用分层求解思想,将模型划分为两层,分别采用混合整数规划方法和机组组合优化方法进行求解。10机测试系统仿真结果表明：所建模型可以提高风能和太阳能的利用率,缓解火电机组的调峰压力,大幅降低风电反调峰特性对电网的影响,从而保证电力系统安全、稳定、经济运行。     

基于改进型GAGA算法的智能电网有功优化调度研究

针对智能电网有功优化调度问题,在传统格雷码加速遗传（GAGA）算法的基础上引入渔夫捕鱼算法来实现迭代算法的收缩搜索;构建Hadoop云平台实现对智能电网有功优化调度的应用。实例验证表明：改进型GAGA算法通过引入农夫捕鱼算法提高了单次迭代时间成本,减少了总的计算迭代次数,降低了总计算时间,同时保证了算法不会陷入局部最优解;在高复杂度的智能电网有功优化调度方面降低了计算量,提高了算法的计算效率。