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.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits. © 2014 Chinese Association of Automation.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Buildings are the main energy consumers across the world,especially in urban communities.Building smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization too.By controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be optimized.All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical energy.In this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes.In this way,large power stations that work with fossil fuels will no longer be developed.The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar panels.Additionally,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work.Different case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar panels.The proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for optimization.Scenario generation and reduction were performed via LHS and backward methods,respectively.Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV system.Based on the results,the proposed model markedly reduced the electricity costs of the smart house.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Analysis and Economic Evaluation of Hourly Operation Strategy Based on MSW Classification and LNG Multi-Generation System

In this study,a model of combined cooling,heating and power system with municipal solid waste(MSW)and liquefied natural gas(LNG)as energy sources was proposed and developed based on the energy demand of a large community,andMSW was classified and utilized.The systemoperated by determining power by heating load,and measures were taken to reduce operating costs by purchasing and selling LNG,natural gas(NG),cooling,heating,and power.Based on this system model,three operation strategies were proposed based on whether MSW was classified and the length of kitchen waste fermentation time,and each strategy was simulated hourly throughout the year.The results showed that the strategy of MSW classified and centralized fermentation of kitchen waste in summer(i.e.,strategy 3)required the least total amount of LNG for the whole year,which was 47701.77 t.In terms of total annual cost expenditure,strategy 3 had the best overall economy,with the lowest total annual expenditure of 2.7730×108 RMB at LNG and NG unit prices of 4 and 4.2 RMB/kg,respectively.The lower heating value of biogas produced by fermentation of kitchen waste from MSW being classified was higher than that of MSW before being classified,so the average annual thermal economy of the operating strategy of MSW being classified was better than that of MSW not being classified.Among the strategies in which MSW was classified and utilized,strategy 3 could better meet the load demand of users in the corresponding season,and thus this strategy had better thermal economy than the strategy of year-round fermentation of kitchen waste(i.e.,strategy 2).The hourly analysis data showed that the net electrical efficiency of the system varies in the same trend as the cooling,heating and power loads in all seasons,while the relationship between the energy utilization efficiency and load varied from season to season.This study can provide guidance for the practical application of MSW being classified in the system.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Green-Aware Token Based Demand Scheduling for Electricity Markets

Increasing consumption, changing nature of loads and the need to reduce carbon emission are some of the factors threatening electricity grid stability and reliability. Demand side management programs mainly work by shifting consumption from peak to off-peak period, which inconveniences some consumers and possibly creates a new peak (Reverse Peak) in off-peak hours. Growing use of Photovoltaic solar power in residences provides an opportunity to manage grid reliability and stability in a more flexible manner, and mitigates reverse peaks. We propose a community based scheduling algorithm that guarantees access to shared power capacity and integrates residences’ solar power into the grid. Results indicate peak demand can be reduced by up to 32.1%, while energy costs can be reduced by up to 14.0%. Furthermore, coordinated discharging can mitigate reverse peaks by up to 23.4%. Encouraging and integrating green energy generation and storage in the consumer side is crucial to grid stability and reliability.

Electricity Generation and the Present Challenges in the Nigerian Power Sector

National development requires adequate electricity supply of which all activities--generation, transmission and distribution leading to it are capital-intensive in terms of funds, natural and human resources. The dwindling power sector government funding coupled with low private sector participation and weak level political will require creative and innovative solutions in addressing the power supply problem in Nigeria. Hence, this paper seeks to examine power sector privatization as a viable option.

Modelling and optimization of combined heat and power system in microgrid based on renewable energy

Due to the short distance between the sources of production and consumption,microgrids(MGs)have received considerable attention because these systems involve fewer losses and waste less energy.And another advantage of MGs is that renewable energy sources can be widely used because these resources are not fully available and can provide a part of the required power.The purpose of this research is to model the MG considering the production sources of microturbines,gas turbines and internal combustion engines.Renewable energies such as wind turbines(WTs)and photovoltaic(PV)cells have been used to provide part of the required power and,because of the lack of access to renewable energy sources at all times,energy reserves such as batteries and fuel cells(FCs)have been considered.The power of the microturbine,gas turbine,internal combustion engine,FC and battery in this system is 162,150,90,100 and 225 kW,respectively.After modelling the studied system,optimization was done using the imperialist competitive algorithm to minimize production costs and provide maximum thermal and electrical loads.The maximum production power for PVs is equal to 0.6860 MWh and at this time this value for WTs is equal to 0.3812 MWh,in which case the excess electricity produced will be sold to the grid.

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

From the perspective of transactive energy, the energy trading among interconnected microgrids(MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power(CHP)-based MGs with demand response(DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally,numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

Prime Energy Challenges for Operating Power Plants in the GCC

There is a false notion of existing available, abundant, and long lasting fuel energy in the Gulf Cooperation Council (GCC) Countries;with continual income return from its exports. This is not true as the sustainability of this income is questionable. Energy problems started to appear, and can be intensified in coming years due to continuous growth of energy demands and consumptions. The demands already consume all produced Natural Gas (NG) in all GCC, except Qatar;and the NG is the needed fuel for Electric Power (EP) production. These countries have to import NG to run their EP plants. Fuel oil production can be locally consumed within two to three decades if the current rate of consumed energy prevails. The returns from selling the oil and natural gas are the main income to most of the GCC. While NG and oil can be used in EP plants, NG is cheaper, cleaner, and has less negative effects on the environment than fuel oil. Moreover, oil has much better usage than being burned in steam generators of steam power plants or combustion chambers of gas turbines. Introducing renewable energy or nuclear energy may be a necessity for the GCC to keep the flow of their main income from exporting oil. This paper reviews the GCC productions and consumptions of the prime energy (fuel oil and NG) and their role in electric power production. The paper shows that, NG should be the only fossil fuel used to run the power plants in the GCC. It also shows that the all GCC except Qatar, have to import NG. They should diversify the prime energy used in power plants;and consider alternative energy such as nuclear and renewable energy, (solar and wind) energy.

Operation Strategy and Matching of Supply and Demand of CCHP System with Various Building Types

The important indications for assessing CCHP(combined cooling,heating and power)systems are their supply-demand matching characteristics between the user demand side and the energy supply side.These characteristics are primarily influenced by different building types and operating strategies.In view of the energy redundancy of the following electric load(FEL)and following thermal load(FTL)operation strategies and the energy deficiency of the following hybrid electric-heating load(FHL)operation strategy,this paper proposes an improved following balanced heat-electrical load(IFBL)operation strategy based on the following balanced heat-electrical load(FBL)operation strategy.Based on the energy utilization rate as the objective function,this paper optimizes the installed capacity of CCHP systems in different buildings and proposes an energy factor for evaluating the supply-demand matching characteristics of the system.The results show that the energy utilization rate and energy factor of the system under IFBL are optimal relative to the other operation strategies.Secondly,the hotel building has the highest energy utilization rate and the lowest energy factor;on the contrary,the office building has the lowest energy utilization rate and the highest energy factor.Finally,the analysis of supply-demand matching for different building types under multiple operating strategies shows that the hospital and hotel systems exhibit optimal supply-demand matching performance under the IFBL strategy,with values of 0.945 and 0.938,respectively;on the contrary,the office system has an optimal supply-demand matching of 0.935 under the FEL strategy.Under the FTL strategy,the systems of all three buildings exhibit poor matching performance.

计及氢能高效利用和热电灵活输出的综合能源系统源荷灵活运行策略

为进一步发挥氢能高效利用优势,构建绿色低碳能源系统,提出一种计及氢能高效利用和热电联产灵活输出的综合能源系统源荷灵活运行策略。首先,对源侧供能模型进行两方面改进:一是引入含风电制氢、燃气混氢、多能用氢和储氢的氢能利用模型,并考虑到电解水和甲烷化反应过程中的热量散失情况,引入热量回收装置,构建计及热量回收的氢能高效利用模型;二是针对常规热电联产灵活性不足的问题,构建含电锅炉和有机朗肯循环的热电灵活输出模型,以解耦常规热电联产“以热定电”和“以电定热”限制。其次,在荷侧引入含可转移、可削减和可平移的电、热柔性负荷,以缓解电、热负荷峰谷差,并与源侧相结合,形成源荷灵活运行模型。最后,综合考虑阶梯型碳交易成本、设备运行维护成本、弃风成本以及购能成本,建立综合能源系统源荷灵活优化运行模型。采用CPLEX求解器对所提综合能源系统源荷灵活运行模型进行求解,并设置不同场景进行对比。结果表明,相比常规氢能利用模型,考虑所提计及热量回收的氢能高效利用模型时,系统的总成本、碳排放量分别降低了1.92%、4.22%,并且引入热电联产改进模型后,其总成本、碳排放量可进一步降低4.08%、7.32%,实现系统低碳、经济和灵活运行。

某纯电驱动重载车辆能耗预测模型

高精度能耗预测模型是准确预测车辆续驶里程的重要前提。针对载荷大幅度变化且非结构化道路运行的纯电驱动重载车辆,建立其组合能耗模型,该模型由能耗计算基本模型与长短时记忆(Long Short-Term Memory,LSTM)神经网络差值修正两部分组成。基于能量流动过程驱动电机和变速器效率建模,结合汽车行驶动力学建立能耗计算基本模型;采用LSTM神经网络来修正基本模型能耗预测结果与车辆典型工况功率测试值的差值,有效提高了大幅变载荷且低信噪比坡度环境下的车辆能耗预测精度,因此组合能耗模型具有参数简单和模型拟合不需解释能耗规律的优点。经试验测试分析,与VT-Micro能耗模型和径向基(Radial Basis Function,RBF)神经网络能耗模型相比,所提组合能耗模型的功率预测平均误差率分别降低了17.76%和3.35%,能够实现纯电驱动重载车辆复杂工况下能耗的准确实时预测。

基于动态电价机制的配网运营商与用户储能商双层协调多目标优化策略

为在电力市场和调度运用中发挥用户侧分布式储能的价值,充分利用电价的优化调节能力,提出考虑负荷需求响应的多目标双层动态电价模型。其中,上层以区域配网代理运营商运营成本和平均负荷波动率最小为目标,下层考虑储能商负荷需求响应,以各储能商运营成本最小为目标。此外,采用改进优劣解距离法对模型求得的Pareto最优解集进行多属性决策,以避免传统优劣解距离法中人为赋权的主观性。最后,决策出最优的内部电价、充放电计划和负荷安排。算例表明,该策略可以降低储能商的运行成本,提高区域配网运营商的收益与区域供电可靠性,同时有利于平滑负荷曲线。

Modeling and optimal energy management of a power split hybrid electric vehicle

With the combination of engine and two electric machines, the power split device allows higher efficiency of the engine. The operation modes of a power split HEV are analyzed, and the system dynamic model is established for HEV forward simulation and controller design. Considering the fact that the operation modes of the HEV are event-driven and the system dynamics is continuous time-driven for each mode, the structure of the controller is built and described with the hybrid automaton control theory. In this control structure, the mode selection process is depicted by the finite state machine （FSM）. The multi-mode switch controller is designed to realize power distribution. Furthermore, the vehicle mode operations are optimized, and the nonlinear model predictive control （NMPC） strategy is applied by implementing dynamic programming （DP） in the finite pre- diction horizon. Comparative simulation results demonstrate that the hybrid control structure is effective and feasible for HEV energy management design. The NMPC optimal strategy is superior in improving fuel economy.

计及阶梯式碳-绿证联合交易和需求响应的生物质能综合能源系统优化

在“30·60”“双碳”目标背景下,生物质能因其储量大、污染小、可再生等优势愈发受到重视。为促进生物质能的有效应用、减少综合能源系统的碳排放量,提出一种考虑阶梯式碳-绿证联合交易机制和综合需求响应下的生物质能综合能源系统优化模型。首先,基于有机朗肯循环技术,对生物质热电联产的关键机理进行研究,构建生物质热电联产模块,同时对系统中其他设备建立数学模型;其次,将多种负荷的综合需求响应机制引入系统,实现电-热-气灵活响应;然后,在阶梯式碳交易机制与绿色证书交易机制的基础上,根据国家可再生能源政策,构建阶梯式碳-绿证联合交易机制;最后,针对6个不同场景的实际算例,从多个角度对比研究生物质成本、阶梯式碳-绿证联合交易机制和综合需求响应分别对系统规划产生的影响。算例结果表明,以生物质热电联产为核心,并考虑阶梯式碳-绿证联合交易机制和综合需求响应的综合能源系统具有良好的经济性和碳减排效果。

源网荷储协同优化的主动配电网日前调度

在“双碳”目标下新能源发展迅速,但其出力的不确定性会影响电网安全稳定运行,主动配电网具备对各类分布式能源组合控制的能力,是解决目前存在的新能源并网问题的有效手段,同时需求响应作为控制负荷侧资源的有效手段,与主动配电网相结合能够有效降低源荷双侧不确定性对电网的影响。构建“源网荷储”协同优化的主动配电网日前优化调度模型,以主动配电网收益最大为目标,综合考虑电动汽车参与需求响应、电网安全、储能电池配置以及各组成单元的成本及约束条件。仿真结果表明:所构建的主动配电网优化调度模型合理,能够降低有功网损,减小尖峰负荷,降低负荷峰谷差,提高配电网收益。