Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

Hybrid LEAP modeling method for long-term energy demand forecasting of regions with limited statistical data

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.

Impact of Economic Transformation on Energy Demand in Southwest China and Forecast

"Economic transformation"has become the main path to promote China's social and economic development,and many regions have increased the importance and attention to"economic transformation",and the Southwest region is no exception.Many cities in Southwest China are developing new energy sources to promote economic development and economic transformation.Economic transformation and economic development in Southwest China are mutually influencing and interacting,while energy development in Southwest China and its local economic development are mutually promoting and influencing,so economic transformation also affects energy demand and development in Southwest China.The importance of economic transformation should be taken into consideration.

Scenario analysis for the energy demand and carbon emissions in low carbon city

This paper takes the climate change and low carbon economy development as the study background, based on the analysis of energy demand and carbon emissions status, which is aimed to provide the low carbon development path in Chinese cities. The method of scenario analysis can be used to predict long-term strategy for the uncertainty future development, and it was introduced to the field of social forecasting and public policy research, such as the environmental strategic planning, policy analysis, and support of decision in resource management, which can be used to explore the possible development trend and target of the results from the macro perspective. Scenario analysis has been gradually applied to the study area on low carbon economy, energy forecasting and other fields in recent years, and there have been many research results in different aspects. This paper takes the scenario analysis as basic study theory, spreading out the present situation of its application in low carbon city and some issues that need further study. As a tool for predicting the future development in low carbon city, the method of scenario analysis has been providing a powerful reference for policies and their executants.

Long-term Energy Demand and CO_2 Problem in the PRC

The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of energy, economy, environment and social development. The total energy demand in 2050 will reach 4.4~ 5.4 billion tce. It is shown in energy supply analysis that coal is China’s major energy in primary energy supply. The share of CO2 emission in the future Chinese energy system will be out of proportion to its energy consumption share because of the high persentage of coal to be consumed. It will reach about 27%. The nuclear option which would replace 30.7% of coal in the total primary energy supply will reduce the share by 9.8%. So the policy considerations on the future Chinese energy system is of great importance to the global CO2 issues.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

Our Daily Life Dependency Driven by Renewable and Nonrenewable Source of Energy

Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.

An Investigation of Coal Demand in China Based on the Variable Weight Combination Forecasting Model

Variable weight combination forecasting combines individual forecasting models after giving them proper weights at each time point. Weight is the type of function that changes with forecast time. A relatively rational description of the system can be proposed with the forecasting method, which is of higher precision and better stability. Two individual forecasting models, grey system forecasting and multiple regression forecasting, were generated based on the historical data and influencing factors of coal demand in China from 1981 to 2008. According to the theory of combination forecasting, the variable weight combination forecasting model was formulated to forecast coal demand in China for the next 12 years.

园区受端新型电力系统电力电量再平衡方法

我国工业园区电能消耗占据了较高的比例,针对依托新型电力系统促进工业园区绿色低碳发展的需要,提出了一种面向工业园区受端新型电力系统的电力电量再平衡方法。首先,开展电力和电量需求预测并进行电力电量初平衡;然后,基于受端源网荷储协同作用并充分考虑园区节能、电能替代、各类分布式电源、储能和需求响应能力的作用进行电力电量再平衡,由此确定园区年度外调电和区内自产电的比例,进一步建立包含低碳效应和电力系统规模变化在内的量化指标评价体系,对电力电量再平衡带来的变配电容量缩减规模和降碳效用进行评价。以我国南方某工业园区新型电力系统的电力电量再平衡为例对以上方法进行了验证,结果表明:该园区2030年变配电规划容量可缩减10.1%,用电综合碳排放因子由0.60 kg/(kW·h)降至0.54 kg/(kW·h);2060年变配电规划容量可缩减9.57%,电能替代实现减碳5.85万t/a,可为受端新型电力系统的电力电量平衡提供有力的理论支撑。

市场环境下考虑全周期经济效益的工业园区共享储能优化配置

为提升用户侧储能运行效率、改善投资成效,提出一种在市场环境下考虑全周期经济效益的工业园区共享储能(SES)优化配置方法。一方面,通过协调不同用户间的差异化调节需求,减少储能容量要求;另一方面,通过整合用户与SES的灵活调节能力,参与需求响应市场拓宽盈利渠道,并且考虑了SES全运行周期经济效益测算以降低投资风险。首先,结合电力市场交易规则,提出了多工业用户组建合作联盟的园区SES运营模式。其次,以运营周期内联盟总成本最小为目标,建立SES双层优化配置模型,其中,上层模型旨在形成最大化投资成效的SES规划方案,而下层模型则综合考虑分时电价、需求响应违约风险等因素形成储能的最优投标调度方式,并结合市场时序演变规律精准量化SES在全运行周期内的运营收益,对上层结果进行修正。接着,利用近似KKT(Karush-Kuhn-Tucker)条件将该模型转化为单层模型进行求解,结合雨流计数法与迭代法量化SES容量衰减对其配置方案的影响,并利用双边Shapley值法分摊各工业用户的投资成本。最后,算例仿真验证了所提方法的有效性,并且分析了储能盈利模式、SES容量衰减以及需求响应违约风险等因素对SES投资经济效益的影响。

考虑预测误差不确定性的源-荷-广义储低碳经济动态优化调度

基于确定性模型的低碳经济调度方法无法准确描述不确定因素对电网碳排放的影响。据此,从系统层面考虑源-荷两侧不确定性,构建了一种低碳经济动态优化调度模型。采用混合高斯概率密度估计刻画风、光预测误差的不确定性,提出一种考虑净负荷预测误差的正负旋转备用容量概率约束方法;为减少系统的碳排放量,一从发电端考虑,引入碳交易机制,建立含有阶梯型碳交易成本的系统总运行成本最低的调度目标函数;二从用户层面考虑,将需求响应负荷同实际储能设备视为广义储能参与日前-日内滚动优化调度;在IEEE39节点系统进行不同场景分析,验证了所提的调度模型实现系统低碳、经济运行目标的有效性,也挖掘了需求响应负荷的减碳潜力。

新型储能金属钒资源需求预测与供应分析

随着全球太阳能、风能等新能源的快速发展,储能作为解决其发电间歇性等问题的必备设施,未来也将迎来爆发式增长。钒液流电池具有本征安全、全生命周期经济性好及环境友好等特点,且已具备产业化应用条件,在储能领域的重要性不断凸显。本文分析了未来钒液流电池的发展趋势,设置了高速发展、参考和低速发展3种情景,结合钒液流电池单位钒用量,预测了未来不同情景下储能领域钒资源需求趋势。研究发现,未来钒资源需求将快速增长,尤其是在高增长情景下,全球2040年钒资源需求将较2021年增长276~338倍;分析全球钒资源的供应情况,认为随着钒液流电池的迅速发展,未来钒资源将供不应求。针对以上情况,研究提出:一是提高钒液流电池的技术研发力度;二是加大钒矿的地质勘查力度,积极探索新的钒矿资源;三是创新钒钛磁铁矿的提钒方法和技术,提高钒资源利用效率;四是加大钒资源的回收利用;五是重视境外钒钛磁铁矿的布局与开发。

中国银矿资源特征及新能源背景下需求分析

【研究目的】银作为重要的贵金属,兼具工业属性和金融属性,对促进国家经济和社会发展至关重要。系统性地分析中国银矿资源特征,研究未来需求趋势,对中国银产业减排布局、资源安全具有重要意义。【研究方法】本文从银产业发展的角度入手,通过分析中国银的应用、地质、供需和市场等特征,总结了中国银矿的资源现状。根据工业银和非工业银的需求驱动差异,运用S−curve和ARIMA模型预测了2022—2035年中国银的需求。【研究结果】根据银矿的资源现状和需求预测结果,得出如下认识:(1)中国银矿具有“四多四少”的地质特征,即总量多、可利用资源少;小矿多、大矿少;贫矿多、富矿少;共伴生矿床多,独立矿床少;(2)随着银应用领域的不断增长,国内的矿山资源和再生资源已经不能满足需求,需进口银矿产品和其他精矿(用于生产副产银,如铅精矿、锌精矿等)来补充缺口;(3)2022—2035年,中国银的需求将持续增长,从5800 t增长到9500 t,年均增长率约为3.7%;(4)2022—2035年,中国工业银需求将迎来较快增长,从3800 t增长到7000 t,年均增长率为4.4%,光伏是拉动工业银需求增长的主要领域;(5)2022—2035年,中国非工业银的需求将缓慢增长,从2000 t增长到2500 t,年均增长率为1.7%。【结论】基于以上研究,为了更好地促进银产业发展,提出如下建议:(1)高度重视并提升银在未来国民经济中的地位和作用;(2)多手段保障银矿资源供应能力;(3)加大技术创新,开展深加工,提高工业银的产品附加值,为能源转型和高端制造提供保障;(4)建立国家与民间双重银储备与保障体系,避免银价大起大落。

光储充建一体站微电网研究综述

为解决电动汽车及新能源大规模接入带来的电力系统运行稳定和新能源高效利用的问题,光储充一体化模式应运而生,其各单元间源荷储协同交互机理及优化调控策略也成为智能电网亟待解决的关键问题。“光储充放+智能建筑”的光储充建一体站微电网模式因其源荷储一体化、供需互补、灵活调度等特征,成为中国节能减碳、能源转型的发展重点。考虑到其微电网运行模式所要面对的分布式能源强不确定性、孤岛并网运行状态下交互机理不明等挑战,对光储充建一体站微电网各单元模块、关键技术、运行状态等方面进行综述,并对光储充建的研究现状进行总结,探讨其未来的发展趋势和需要面对的挑战。研究成果对挖掘经济激励下各类需求响应资源的调控潜力,保障电网供电可靠性,具有重要理论和实际参考意义。

需求响应的园区综合能源系统优化运行方式研究

为了更好地展现园区综合能源系统中多种能源网络耦合互补、协调配合的特点,有效提高系统运行的经济性,提出了在电、热、冷、气4种网络负荷约束下考虑综合需求响应的冷热电联供型园区综合能源系统优化运行模型。首先在某园区建立电、热、冷、气4种能源网络,以及建立系统内部多种能量转换设备和储能装置的数学模型,同时考虑供热/供冷系统具有的热惯性,在此基础上,以综合能源系统建设运行成本与综合考虑价格型、激励型、替代型需求响应补偿成本之和最小为目标函数构建优化调度模型,并采用MATLAB平台中的Gurobi求解器对所提模型进行求解。通过算例分析可以得到:在能更好体现多能耦合互补特点的4种网络构成的园区综合能源系统中,综合考虑价格型、激励型、替代型需求响应的方式起到对负荷进行“削峰填谷”的作用,能更好地缓解系统高峰时期的供能压力,更有效地降低园区综合能源系统的日内运行成本,使得系统运行的总成本降低5.7%,有效提高了系统运行的经济性。

基于季节性指数平滑法的电能表需求预测分析

随着智能电能表大规模普及应用,电能表需求数量日益庞大,采用经验人工估计容易造成电能表临时短缺和库存积压等问题。基于计量系统中的电能表历史安装数据,采用简单季节性模型、Winters加法模型和Winters乘法模型,并结合LSTM模型对结果进行对比分析,比较不同模型的优劣,确定电能表需求预测最优方法。实证结果表明:Winters乘法模型预测效果最好,预测值平均误差为2.46%,预测趋势符合实际情况。Winters乘法指数平滑法能够对电能表需求变化趋势进行科学合理预测,可以协助供电公司更加高效开展电能表运营管理,提高计量资产利用效率。

计及[火用]效率和供需双侧灵活响应的园区综合能源系统低碳经济调度

为实现园区综合能源系统的低碳经济运行及能源高效利用,本文提出了一种考虑[火用]效率和供应、需求双侧灵活响应的园区综合能源系统优化调度模型.首先,本文提出一种基于[火用]效率的园区综合能源系统低碳减排方法,该方法对碳排放的约束性强且无参数设置问题,可以实现降低系统碳排放量、减少能源品质损耗的双目标;其次,考虑系统中供应侧响应和需求侧响应,将二者与[火用]效率相结合进行协同优化,提升系统运行的灵活性;然后,以降低系统运行成本和提高[火用]效率为目标函数,建立一种经济、低碳、高效的园区综合能源系统优化调度模型;最后,通过设置多个运行场景,对比验证所提模型的有效性.算例结果表明,该模型能够兼顾系统的经济性、低碳性和能源优质利用性,可为园区综合能源系统低碳经济调度运行提供参考.

计及需求响应的MPIES多时空尺度鲁棒随机优化调度

针对多园区综合能源系统多时间尺度优化调度问题,结合各时间尺度源、荷不确定性特征与响应特性、园区数目对计算规模的影响,提出一种计及需求响应的多园区综合能源系统多时空尺度鲁棒随机优化调度架构。日前调度建立计及可中断负荷的多园区多能流系统鲁棒优化调度模型,确定系统最恶劣运行场景下基本调度策略;日内调度建立计及可中断负荷和替代型需求响应的单园区多能流系统随机优化调度模型,细化并调整调度策略;实时调度建立计及直接负荷控制的单园区电气系统随机优化调度模型,削弱调度策略受源、荷出力波动的影响。最后,通过算例验证了该架构能有效提高系统运行的经济性和稳定性。

基于目标级联法的园区综合能源系统多主体日前经济优化调度

园区综合能源系统运营商和用户主体之间存在复杂的利益博弈关系,因此研究如何在提高系统整体经济效益的同时平衡各方主体利益具有必要性。为此,提出一种考虑多主体利益的园区综合能源系统日前经济优化调度策略。考虑利用园区综合能源系统多能协同互补优势参与需求响应市场交易,建立两级递阶经济优化调度模型:上层是以运行利润最大为目标的运营商优化调度模型,下层是以用能成本最小为目标的用户优化响应模型;采用基于目标级联法的分布式优化算法实现上下层模型的解耦和独立并行求解。通过算例分析,验证了所提策略通过综合需求响应可实现园区供、用能侧可调资源的协同优化,并通过分布式求解使各主体经济效益均达到最优。

园区供电系统广义负荷需求响应建模与分析

产业园区供能系统采用的是多能耦合、源网荷储协同优化的方式,其电力负荷定义也转变为新型综合性的广义负荷。考虑电价信号对园区主动负荷响应特性的影响,建立了计及响应电价的园区供电系统广义负荷模型,并采用层次聚类方法提取典型负荷模式,研究园区供电系统负荷需求响应特征。为验证模型与方法的有效性,采用中国南方城市某园区供电系统负荷进行测算,模拟了实时电价和分时电价2种市场机制下主动负荷的响应特性。分析了不同主动负荷占比对园区广义负荷特征的影响,并定量讨论了不可控负荷、可转移负荷、可中断负荷对广义负荷需求响应效果的贡献程度。结果表明,适当的电价机制将有序引导园区供电系统广义负荷主动削峰填谷,有助于虚拟电厂建设,实现源网荷灵活互动。