基于满意度的户用型微电网多属性需求响应策略

针对户用型微电网的负荷多样性、用户侧参与需求响应过程中满意度有待提高的问题,结合用户侧需求响应理论与数据挖掘的思想展开研究。首先,以用户综合满意度指数最大为目标函数,建立基于满意度的多属性需求响应策略模型,从用户的历史用电数据中挖掘用电行为习惯。其次,在传统细菌群体趋药性算法中引入禁忌搜索,提出禁忌细菌群体趋药性混合算法(BCCTS),对模型求解。最后,通过实例验证获得最优的微电网负荷用电计划。算例结果验证了所提出模型与求解算法的有效性,能够帮助用户在削减电力费用的同时减小电网峰谷差;BCCTS算法能够得到全局最优解,适用于需求响应策略模型的求解。     

智能电网下多时间尺度家庭能量管理优化策略

建立负荷在功率约束与需求响应约束下的激励需求响应模型以及含分布式电源、储能与电动汽车的家庭用电模型,在预测模型多时间尺度能量管理的基础上,以最小化用户自身用电费用与买电功率波动的两层目标函数实时优化调整策略。通过实时调整储电池、电动汽车的充放电,从而保证用户购电满足需求相应的要求。最后采用改进的粒子群算法对多时间尺度目标函数进行求解,并且与原始的粒子群算法进行对比,结果表明所提算法可显著降低用户的用电费用与功率波动。     

基于需求响应的用户侧微电网多目标优化运行方法

摘要： 需求响应使用户积极参与到电网的优化运行中,是实现用户侧微电网优化运行的重要手段。针对用户侧微电网,提出了基于价格激励与可控负荷的优化运行模型。模型中包括实时电价的分区策略与可控负荷的调控策略。将用户用电成本,空调与热水器温度与目标温度的差值最小化作为优化目标,将可延迟负荷的延迟时间,可计划负荷的工作状态作为决策变量。通过仿真计算与分析,验证了优化模型的可行性与有效性,为用户侧微电网的低成本运行提供了理论支持。     

需求侧管理下居民生活用电的系统动力学模型

针对我国电力短缺严重问题,基于系统动力学理论,分析了需求侧管理对居民生活用电行为的影响,构建了需求侧管理下居民生活用电系统动力学模型,对其进行动态仿真和模拟。结果表明,在需求侧管理的作用下,居民用户用电行为得到优化,用电成本降低。     

基于负荷类型细分的负荷聚合商日前投标非合作博弈模型

负荷聚合商能整合中小负荷资源并为其提供参与市场调节的机会,以提高需求响应效率。提出了一种基于负荷类型细分和非合作博弈的负荷聚合商日前投标模型,一方面根据居民柔性负荷的不同用电特性进行典型分类并将每一类负荷作为研究对象,避免了以家庭为控制单位参与投标的复杂度;另一方面在开放电力市场下,不同于常规多目标集中优化,引入博弈思想协调优化各聚合商作为独立决策主体的利益。根据物理特性将居民柔性负荷分成4类,并基于用户使用习惯和舒适度建立负荷模型;在市场电价的引导下,基于用户黏性理论优化聚合商售电电价,建立负荷聚合商日前投标决策模型并进行算例分析。仿真结果表明,所建立的负荷聚合商投标决策模型通过对居民负荷的分类聚合和补偿,在计划调度层面能有效减少电网峰时负荷和平抑峰时电价,提高负荷聚合商的利润,降低用户用电成本。     

居民用户能效管理技术应用

文章采用三层架构的方式,对能效管理关键设备、融合通信技术、用能管理主站进行详细描述,结合浙江海盐智能小区示范工程,通过安装能效管理设备,部署主站软件,实现对居民用户的用电设备、微网接入设备等进行用电信息的采集和管理,为用户提供优化的科学用电方案.结果表明,能效管理技术具有实用、高效、节能的特点,实现了电力用户与电网之间...     

考虑含多种可控负荷的社区冷热电联供系统协同优化调度

针对社区冷、热、电三联供(CCHP)系统需求侧可控负荷的用能特性,从满足一个调度周期内各时刻供需平衡的角度,建立了可控负荷以及各供能网络模型。以经济、环境等方面的综合运行成本最小为目标,最终建立了社区CCHP系统的日前优化调度模型。通过Matlab和Cplex求解器对算例中可控负荷的优化及各机组的出力进行求解,得到夏季典型日下3种场景的可控负荷参与优化调度的结果。算例仿真分析了对CCHP系统中可控负荷的优化调度在减少系统综合运行成本、减小负荷峰谷差值等方面的作用,验证了该优化调度模型的有效性。     

基于改进精简爆炸烟花的区域商业楼宇日前电能交易

由于现在越来越多的商业楼宇配备了储能和微型燃气轮机等分布式可控资源,能够根据需求转移负荷、减少负荷甚至发电上网,具备了小范围区域电能交易的可能。针对小范围区域电能交易问题,该文搭建了考虑集成商和商业楼宇的电能交易模型进行研究。其中,集成商根据楼宇用电策略改变分时电价提高收益,商业楼宇根据电价改变用电策略降低成本;同时,针对集成商与商业楼宇之间存在隐私问题,该文提出了一种基于改进精简烟花算法（improved bare bones fireworks algorithm,IBBFWA）的交替求解方法,实现模型的求解。通过算例结果验证模型对降低商业楼宇运行成本的有效性和算法的高效性。     

考虑用电优质系数的微电网分段混合策略优化调度

考虑需求侧响应的并网型微电网调度常忽略光伏出力特性对可控负荷调度的影响,且缺乏完善的需求侧评价指标。文章提出一种PVP&LS(Photovoltaic Priority&Load Shifting)分段混合调度策略,PVP策略依据预测光伏出力,以弃光率最小为目的调控可控负荷;LS策略以降低峰谷差率为目的,对上阶段剩余可控负荷移峰填谷。同时,提出了改进的用电优质系数模型,不仅避免调控过度,还可量化用户感受。最后通过自适应遗传算法仿真对比,运行成本减少了3.91%,用电优质系数增加了4.09%,弃光率下降了10.2%。     

基于用户需求的居民小区电动汽车充放电优化控制策略

文章提出了一种考虑用户需求和利益的居民小区电动汽车有序充放电策略。该策略基于分时电价机制,对现有负荷模型进行优化,并建立新的用户需求和利益模型;采用莫楞贝突变遗传算法求解每辆电动汽车的最优充放电时间;采用蒙特卡洛模拟法做出相应负荷曲线;以某个小区为例进行算例计算和分析验证。研究结果表明,该方法可防止变压器过载,其峰谷差率比无序充电降低了24.95%,用户平均充电成本降低了50%。     

Integrated demand response based on household and photovoltaic load and oscillations effects

Due to the attractiveness of household gas-electric tools, in this paper, an optimization technique is suggested based on the integrated demand response (IDR) and degree of tolerance for household energy management. The proposed method is mostly used to express the dynamic change in the forms of energy and undetermined variables in the systems, resulting from household and photovoltaic (PV) load. Thermostatically controlled demands include gas-electricity and air conditioning, and cut-able loads include gas-electric stove and washing machine. The interval optimization is modeled for optimizing the operation and greenhouse gas emission costs in multi-purpose systems. The undetermined variables are formulated as interval statistics and the limitations are simplified by degree of tolerance. In order to solve it, the interval optimization technique is converted into certainty optimization with the interval order relationship and the delayed probability degree. Then, the developed grasshopper search algorithm is based on the chaos theory to solve the interval optimization model in order to respond to uncertainty and demands of the users, such that degree of tolerance of cost that is acceptable by users is optimized. Contrary to other optimization algorithms, the grasshopper search algorithm can be combined with other methods. In this paper, the chaos theory is adopted to find a better solution. Since the information is placed in the search space without order, using this technique considerably leads to good convergence speed, precise final solution finding, not being trapped in local minima, lower SD, and robustness. Both methods of IDR and degree of tolerance for the household gas-electric equipment manage to reduce energy consumption by about 25% compared to traditional methods.     

A framework for home energy management and its experimental validation

With the Smart Grid revolution and the increasing interest in renewable energy sources, the management of the electricity consumption and production of individual households and small residential communities is becoming an essential element of new power systems. The electric energy chain can greatly benefit from a flexible interaction with end-users based on the optimization of load profiles and the exploitation of local generation and energy storage. This paper proposes a framework for the development of a complete energy management system for individual residential units and small communities of domestic users, taking into account both the power system and the final users’ perspectives. All the main elements of the framework are considered, and contributions are provided on the users’ habits profiling, electricity generation forecast, energy load, and storage optimization. Specifically, we propose a linear regression model to predict the photovoltaic panels production, a stochastic method to forecast the home appliances usage, and two optimization models to optimize the electricity management of residential users with the goal of minimizing their bills. The study shows that it is possible to reduce the energy bill of residential users through the electricity optimization driven by dynamic energy prices. Moreover, remarkable improvements of the electric grid efficiency can be achieved with the cooperation among users, confirming that services for the coordination of the demand of groups of users allow huge benefits on the power system performance.     

基于NCPSO的微网群优化调度策略研究

传统的粒子群优化（PSO）算法因在微网优化中不易达到全局最优而导致微网运行成本过高,该文采用小生境混沌粒子群优化（NCPSO）算法对混合微网群的运行策略进行协同优化,以实现区域微网经济性最优、环境治理成本最低、风光等可再生能源利用率高等目的。根据所提出的调度策略,建立的优化调度模型包括动态电价下的负荷模型、经济收益模型以及成本模型等,使用NCPSO算法得到多微网在一个周期内的最佳运行状态,实现微网群系统综合能源的互动调控、空间互补。通过分析微网群的功率交互动态、可控能源的发电以及储能电池的荷电状态等,验证微网群的电力负荷响应动态电价,表明了NCPSO算法优化微网群运行的优越性、有效性。     

基于分布式算法的实时电价策略研究

在智能电网中,实时电价(RTP)是解决智能电网供需平衡的理想手段。通过分析国内外实时电价机制发展现状,将家庭用户负荷分为四类,综合考虑用户间的不同用电特性,构建了相应的用电效益优化模型,采用分布式算法,结合某地区的具体数据,并针对不同的需求响应方案、蓄电池成本、系统大小对模型进行仿真。结果表明,基于分布式算法的需求响应实时电价策略可使社会用电效益最大化。     

Suitable operational strategy for power interchange operation using multiple residential SOFC (solid oxide fuel cell) cogeneration systems

A suitable operational strategy for a power interchange operation using multiple residential solid oxide fuel cell (SOFC) cogeneration systems for saving energy is investigated by an optimization approach based on mixed-integer linear programming. In this power interchange operation, electricity generated by residential SOFC cogeneration systems is shared among households in a housing complex without allowing a reverse power flow to a commercial electric power system in order to increase electric load factors of the system. For an SOFC cogeneration system operated continuously with the minimum output, two types of operational strategies for the power interchange operation are adopted: an operation to meet the total demand for electricity in intended households by the electricity output of SOFC cogeneration systems and an operation to meet the demand for hot water in each household by the hot water output of the SOFC cogeneration system. To clarify a theoretical limit of the energy-saving effects of the two strategies, this study numerically analyzes optimal operation patterns for 20 households on three representative days. The results show that the former operational strategy, which takes advantage of the high electricity generating efficiency of the SOFC, is more suitable for saving energy as compared to the latter strategy.     

含分布式电源的配电网动态无功补偿优化策略研究

兼顾分布式电源（DG）出力和负荷动态变化提出一种基于需求侧响应的配电网动态无功优化策略。该策略采用灰色关联度映射方法划分时段,在同一时段内协同优化不同类型变量,然后固定多个时段内并联电容器组（SCB）和有载调压变压器（OLTC）的状态,进行二次静态无功优化校正。针对模型特征,对不同类型变量采用混合协同进化算法进行求解,并提出基于Tent混沌映射和Levy飞行策略的改进麻雀算法提高协同求解效率。仿真结果表明：计及新能源出力变化和需求侧响应的所提策略能在降低求解规模的同时获取较高满意度的无功优化结果,且该混合协同进化算法在求解混合整数的非凸、非线性优化问题上具有一定优势。     

The barriers to energy efficiency in China: Assessing household electricity savings and consumer behavior in Liaoning Province

This article investigates the barriers to energy efficiency at the residential sector within one province in China and explores patterns of household electricity consumption. The article presents the results of a survey questionnaire distributed to more than 600 households in Liaoning Province, field research at various Liaoning government agencies, and research interviews of Liaoning government officials to determine the efficacy of their energy efficiency efforts in China. It then investigates the extent that electricity consumers have taken advantage of energy efficiency opportunities relating to more efficient lights, water heaters, appliances, air-conditioners and heaters, and better energy-efficiency labels. The article also assesses the degree that electricity users have become more aware about electricity prices and their levels of consumption, and touches on the connection between rising levels of income and electricity use. It concludes by providing recommendations for how to improve efforts to promote conservation and reduce electricity load growth in Liaoning Province and beyond.     

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。