新能源电力系统的需求侧资源利用关键技术分析

本文简要介绍了基于新能源下全新电力平台需求侧资源整合与调度的关键技术：系统安防技术,源网荷储多层次、多主体的协同调度技术,各级电力平台的协同规划技术,大基数通信交互技术;开展了基于新能源下需求侧资源分配的智慧用电管理分析：建立智慧用电资源分配的数字模型,对各场景进行用电调度分析,生产设施用电需求的系统分析,温控设施电力需求的系统分析。经算例分析发现：DR计划中,引入新能源,联合电价引导机制,能够有效改善用电负荷,控制需求侧整体的用电成本。     

应对电荒绿色消夏专家支招

据中电联估计2011年度夏电力缺口巨大,国家发展改革委要求力保居民生活用电。那么,打好迎峰度夏这一仗,电力部门、工业企业、机关单位和广大居民又该如何作为?加强需求侧管理,错峰用电,实施峰谷电价,除此之外,专家建议,电力蓄能调峰和节电管理并举,节能先行。     

基于需求响应的居民可控能效负荷优化

为解决居民生活用电需求不断增加,居民生活用电方式不合理造成能源浪费越来越严重的问题,从居民用户的可控能效负荷入手,对典型可控能效负荷空调、热水器和照明负荷进行分析,建立负荷能耗数学模型,根据其运行特性,结合居民用户用电习惯和分时电价,制定居民可控能效负荷优化策略;建立以居民用户用电成本和用电满意度为目标的优化模型。为提高和声搜索算法的求解速度与计算精度,对其参数进行动态调整,并与差分进化算法进行融合,应用于可控能效负荷的优化求解。算例结果表明了改进算法具有较好的收敛性和较高的准确性,验证了居民可控能效负荷优化策略的可行性,实现了从需求响应的角度对可控能效负荷进行优化管理的思想。     

智能用电技术对需求侧管理的影响

智能用电技术有效促进了电力需求侧管理的发展,使需求侧管理被赋予了全新的意义,其作用也发生较大的改变.分析探讨高级测量体系(AMI)对自动需求响应项目、智能家居/小区等智能用电系统对能效管理、电动汽车接入对负荷管理和电网电压稳定性、以及分布式能源和微电网接入对节电节能的影响.这些典型技术的应用将有助于实现智能需求侧管理,促进用户主动参与节电,达到削峰填谷、提高系统稳定性和节能减排的目的.     

城乡居民生活用电状况的比较分析

本文基于对秦皇岛城市居民与乡村居民的电力市场调查资料的比较分析,着重阐述了乡村居民用电需求市场的发展潜力,分析了影响乡村居民用电增长的几种主要因素,并提出了开拓和发展乡村居民生活用电市场的一些具体措施与对策。     

建立电力需求侧管理的长效机制

介绍了电力需求侧管理的基本概念,阐述了电力需求侧管理与节约用电、计划用电在概念和做法上的不同,对建立电力需求侧管理的长效机制提出了四点建议。     

基于FDA的居民用户空调用电行为分类分析方法

针对居民空调用电行为分类中存在事件型数据,导致分类分析耗时长、结果不准确等问题,提出一种基于函数型数据分析(FDA)模型的居民空调用电行为分类分析方法.该方法采用多重分形理论提取居民用电行为特征,使用函数型数据分析算法对居民空调用电行为进行聚类后获取居民空调用电行为类别,采用改进动态时间规整算法对居民空调用电行为实施分...     

一种考虑居民经济承受力的生活用电电价可调整空间模型

从考虑居民经济承受能力的角度提出了一种基于需求弹性理论的居民生活用电电价可调整空间模型。分析了电费与居民生活电价、居民人均年收入的关系,提出了电费变化的弹性分解模型,并提出了居民对电费变化可承受能力的模型。在这2个模型基础上,导出了居民生活用电电价可调整空间模型的数学公式。算例分析表明,该模型切实可行,具有实际意义,为政府相关部门制定居民电价政策提供了考虑社会稳定、社会居民方面的理论依据。     

能源管理新模式在企业用电精细化管理中的应用

随着能源互联网概念的提出以及用电监测设备价格的下降,将其相结合可为企业用户自有的变电站及配电网提供专业的能源管理。传统企业用户变电站的管理模式存在诸多问题,能源管理新模式提出了针对电力需求侧用电管理的客户电力管理系统,利用客户侧用电精细化管理实现企业用电成本的精确核定。介绍了能源管理新模式的核心技术云计算,详细分析了能源管理新模式的组成和主要功能。通过在某企业的实际应用,将企业局部用电变为系统用电,实现了企业智能化能效管理的目标。     

（二）电力需求侧管理（DSM）

1电力需求侧管理（DSM）概念：电力需求侧管理，是指通过提高终端用电效率和优化用电方式，在完成同样用电功能的同时，减少电量消耗和电力需求，达到节约能源和保护环境、实现低成本电力服务而进行的用电管理活动。其主要内容是对终端用户进行负荷管理、提高能源使用效率及实现综合资源规划等。     

Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey

China’s residential electricity demand has grown rapidly over the last three decades and given the expected continued growth, demand side management (DSM) can play an important role in reducing electricity demand. By using micro-level data collected from 1450 households in 27 provinces in the first-ever China Residential Energy Consumption Survey in 2012, this study estimates the effects of three DSM measures empirically: tiered household electricity pricing, China Energy Label program, and information feedback mechanisms. We find these measures have contributed to moderating residential electricity demand growth but additional policy reform and tools are needed to increase their effectiveness and impact. Residential electricity demand is found to be price- and income- inelastic and tiered pricing alone may not be as effective in electricity conservation. The statistically significant relationship between China Energy Label efficient refrigerators - but not televisions - and lowered residential electricity consumption reflect mixed program effectiveness. Lastly, of the information feedback currently available through electricity bills, payment frequency and meters, only meter reader is estimated to be statistically significant. Important policy implications and recommendations for improving each of these three DSM measures to expand their impact on reducing residential electricity consumption are identified.     

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.     

A systems approach for sizing a stand-alone residential PEMFC power system

Polymer electrolyte membrane fuel cells (PEMFCs) show great promise in portable, automotive, and stationary applications. They have reached the test and demonstration phase in automotive and power markets today. This paper is focused on a stand-alone residential PEMFC power system that provides the electricity needs of the house. A novel stochastic sizing methodology is developed that considers both fuel cell system dynamics and residential load dynamics in overall system sizing for the stand-alone residential fuel cell power system. Understanding the nature of demand side is critical in stand-alone system sizing. Thus, experimental measurements have been completed to capture the load side dynamics in detail. No such data is found in the current literature. The Threshold Bootstrap method is used to model the residential load demand and to produce many realistic load profiles. Matlab/Simulink is used to run system simulations to determine system sizes based on parameters defined through a designed experiment. Comparison between the proposed sizing method and a possible worst case scenario sizing is given. The new sizing methodology can be used together with sophisticated demand analysis programs to obtain customized sizing for each user as stand-alone power systems become more viable.     

Demand-side management by electric utilities in Switzerland: Analyzing its impact on residential electricity demand

In this paper, we use panel data from a survey conducted on 30 utilities in Switzerland to estimate the impact of demand-side management (DSM) activity on residential electricity demand. Using the variation in DSM activity within utilities and across utilities over time we identify the impact of DSM programs and find that their presence reduces per customer residential electricity consumption by around 5%. If we consider monetary spending, the effect of a 10% increase in DSM spending causes a 0.14% reduction in per customer residential electricity consumption. The cost of saving a kilowatt hour is around 0.04CHF while the average cost of producing and distributing electricity in Switzerland is around 0.18CHF per kilowatt hour. We conclude that current DSM practices in Switzerland have a statistically significant effect on reducing the demand for residential electricity.     

Response of residential electricity demand to price: The effect of measurement error

In this paper we present an empirical analysis of the residential demand for electricity using annual aggregate data at the state level for 48 US states from 1995 to 2007. Earlier literature has examined residential energy consumption at the state level using annual or monthly data, focusing on the variation in price elasticities of demand across states or regions, but has failed to recognize or address two major issues. The first is that, when fitting dynamic panel models, the lagged consumption term in the right-hand side of the demand equation is endogenous. This has resulted in potentially inconsistent estimates of the long-run price elasticity of demand. The second is that energy price is likely mismeasured.To address these issues, we estimate a dynamic partial adjustment model using the Kiviet corrected Least Square Dummy Variables (LSDV) (1995) and the Blundell–Bond (1998) estimators. We find that the long-term elasticities produced by the Blundell–Bond system GMM methods are largest, and that from the bias-corrected LSDV are greater than that from the conventional LSDV. From an energy policy point of view, the results obtained using the Blundell–Bond estimator where we instrument for price imply that a carbon tax or other price-based policy may be effective in discouraging residential electricity consumption and hence curbing greenhouse gas emissions in an electricity system mainly based on coal and gas power plants.     

峰谷分时电价对用户满意度影响的系统动力学分析

峰谷分时电价作为电力需求侧管理的一种有效调峰手段,其实施会对用户的用电方式满意度和电费支出满意度产生一定的影响,从而最终影响用户对峰谷分时电价的综合满意度。从系统和运动的观点出发,分析了不同影响因素间的因果关系,基于系统动力学方法构建了峰谷分时电价对用户满意度影响的系统流程图,从峰谷电量变化、用户电费节约及用户满意度3个方面分析了峰谷分时电价对用户满意度的影响,并对不同峰谷电价比和响应敏感型用户进行了敏感性分析。结果表明,模型合理、有效。     

Residential and non-residential electricity dynamics

This paper uses U.S. panel data to instrument and examine the dynamics of electricity within the world market while separating between both residential and non-residential electricity consumptions during the time period of 1990–2014. To better assess the true differences within each causal relationship, all panel data has been separated into one Full panel and three subpanels of High, Middle, and Low income. The empirical framework used consists of various tests that identify the existence of cross-sectional dependency, a Pesaran panel unit root test, a Westerlund panel cointegration test, and the Dumitrescu–Hurlin method of the Granger causality test. Furthermore, this paper utilizes DOLS to estimate any long-run elastic relations between real GDP and residential or non-residential electricity consumption. Based on the results, this paper determines that no long-run relationship exists between non-residential electricity consumption and economic growth throughout and that the relationship between residential electricity consumption and economic growth possesses unit elastic behavior in the long run. Other findings throughout imply causality moves from economic growth in the direction of residential electricity consumption for all panels.     

Residential electricity demand in Spain: New empirical evidence using aggregate data

This paper presents an empirical analysis on residential demand for electricity. This analysis has been performed using aggregate panel data at the province level for 47 Spanish provinces for the period from 2000 to 2008. For this purpose, we estimated a log–log demand equation for electricity consumption using a dynamic partial adjustment approach. This dynamic demand function has been estimated using a two-step system GMM estimator proposed by Blundell and Bond (1998). The purpose of this empirical analysis is to highlight some of the characteristics of Spanish residential electricity demand. Particular attention has been paid to the influence of price, income, and weather conditions on electricity demand. The estimated short and long-run own price elasticities are negative, as expected, but lower than 1. Furthermore, weather variables have a significant impact on electricity demand.     

Price freezes, durables, and residential electricity demand. Evidence from Greater Buenos Aires

This paper examines the determinants of residential electricity demand in Greater Buenos Aires between 1997 and 2006. During the second half of this period, residential electricity tariffs remained nominally fixed, while rising incomes increased sales of durables. Our study is one of few that use monthly data to examine the contribution of prices to residential consumption growth, and it appears to be the first time-series study to explicitly consider the impact of air conditioners on residential demand. Results indicate that durables have an impact on residential electricity demand. Simulations illustrate how prices, income, and durables impact future demand.     

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.