Coordinated restoration optimization of power-gas integrated energy system with mobile emergency sources

In an integrated energy system(IES) composed of multiple subsystems, energy coupling causes an energy supply blockage or shutdown in one subsystem, thereby affecting the energy flow distribution optimization of other subsystems.The energy supply should be globally optimized during the IES energy supply restoration process to produce the highest restoration net income. Mobile emergency sources can be quickly and flexibly connected to supply energy after an energy outage to ensure a reliable supply to the system, which adds complexity to the decision. This study focuses on a powergas IES with mobile emergency sources and analyzes the coupling relationship between the gas distribution system and the power distribution system in terms of sources, networks, and loads, and the influence of mobile emergency source transportation. The influence of the transient process caused by the restoration operation of the gas distribution system on the power distribution system is also discussed. An optimization model for power-gas IES restoration was established with the objective of maximizing the net income. The coordinated restoration optimization decision-making process was also built to realize the decoupling iteration of the power-gas IES, including system status recognition, mobile emergency source dispatching optimization, gas-to-power gas flow optimization, and parallel intra-partition restoration scheme optimization for both the power and gas distribution systems. A simulation test power-gas IES consisting of an 81-node medium-voltage power distribution network, an 89-node medium-pressure gas distribution network, and four mobile emergency sources was constructed. The simulation analysis verified the efficiency of the proposed coordinated restoration optimization method.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

For heating systems based on electricity storage coupled with solar energy and an air source heat pump(ECSA),choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency.In this paper,four cities in three climatic regions in China were selected,namely Nanjing in the hot summer and cold winter region,Tianjin in the cold region,Shenyang and Harbin in the severe cold winter region.The levelized cost of heat(LCOH)was used as the economic evaluation index,and the energy consumption and emissions of different pollutants were analyzed.TRNSYS software was used to simulate and analyze the system performance.The Hooke-Jeeves optimization algorithm and GenOpt software were used to optimize the system parameters.The results showed that ECSA systemhad an excellent operation effect in cold region and hot summer and cold winter region.Compared with ECS system,the systemenergy consumption,and the emission of different pollutants of ECSA system can be reduced by a maximum of 1.37 times.In cold region,the initial investment in an air source heat pump is higher due to the lower ambient temperature,resulting in an increase in the LOCH value of ECSA system.After the LOCH value of ECSA system in each region was optimized,the heating cost of the system was reduced,but also resulted in an increase in energy consumption and the emission of different pollutant gases.

Cost Optimization Modeling of Renewable Energy Sources in Smart-Grid Using SCADA

In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.

Optimal dispatch approach for rural multi-energy supply systems considering virtual energy storage

In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China,this study proposes an optimal dispatch approach for a rural multi-energy supply system(RMESS)considering virtual energy storage(VES).First,to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes,this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint.Subsequently,an RMESS model encompassing these two types of VES was formulated.This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations.Based on the above models,a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation.Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.

Emergency Energy Management of Microgrid in Industrial Park Basedon Robust Optimization

Reducing the impact of power outages and maintaining the power supply duration must be considered in implementing emergency energy dispatching in micro-networks.This paper studies a new emergency energy treatment method based on the robust optimal method and the industrial park micro-network with the optical energy storage system.After controlling the load input,a control strategy of adjusting and removing is proposed.Rolling optimal theory is applied to emergency energy scheduling based on a robust optimal mathematical model.A weighting factor is introduced into the optimal model to balance the importance of reducing and retaining the power supply.Uncertainty is designed to adjust the effect of uncertainty on the problem.The example shows that this method can flexibly set the weight coefficient and uncertainty value according to the actual situation so that the input of the control load can be optimized.

Modeling and Optimization of Solar Collector Design for the Improvement of Solar-Air Source Heat Pump Building Heating System

To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.

Use of rural energy resources and eco-environmental degradation in Tibet

Cattle dung, firewood, and crop straw have being used as survival necessities by farmers and herdsmen for thousands of years in Tibet. Until recently such biotic energy source still constitutes more than 92 per cent of total rural energy consumption due to lack of petroleum, coal and new alternative energy sources. As a result, environmental degradation such as land desertification, soil erosion, grassland degradation and soil fertility reduction is increasingly aggravated, the area of desertified land has increased 1467.5 km2 from 1991 to 1997. Degraded area of grassland has reached 2.60×107hm2, increased by 116.1% from 1987 to 1996. To prevent further deterioration of eco-environment in Tibet great efforts should be made to make full use of ample solar energy, wind energy and other biotic energy of the Qinghai-Tibet Plateau. The solar cooking stoves and solar hothouse, expand forest area and replace existing abiotic energy sources with firewood forest should be popularized. This is an urgent task to protect the eco-environment of Tibet today.

Adjusting and Optimizing Structure of Energy Sources for Power Generation of Fujian Province in the 21st Century

The author puts forward the pattern of optimizing the structure of energy sources for generating power in the early stage of the 21st century in Fujian Province; analyzes imper’tant functions on speeding up nuclear power for adjusting the structure of energy sources and heightening economic benefits.and suggests that the first liquefied natural gas combined-cycle power plant will start to build at the end of this century and every effort is made so as to change the recent unreasonable structure of energy source step by step and form the optimized structure of energy sources for generating power, that includes hydropower, thermal power (coal, oil and natural gas), nuclear power, pumpedstorage power, and power from new energy sources. In order to reach the abovementioned significant target, the author discusses the technical and economic measures and the supporting policy to be taken at present and in future.

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.

Smart optimization in battery energy storage systems:An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources(RESs)are considered to be reliable and green electric power generation sources.Photovoltaics(PVs)and wind turbines(WTs)are used to provide electricity in remote areas.Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment.The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution.This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization(TLBO)named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost(TAC).The reliability of the system is considered by a maximum allowable loss of power supply probability(LPSPmax)concept.The results obtained from the JLBO algorithm are compared with the original Jaya,TLBO,and genetic algorithms.The JLBO results show superior performance in terms of TAC,and the PV–WT–battery hybrid system is found to be the most economical scenario.This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems.

Load Frequency Control of Multi-interconnected Renewable Energy Plants Using Multi-Verse Optimizer

A reliable approach based on a multi-verse optimization algorithm(MVO)for designing load frequency control incorporated in multi-interconnected power system comprising wind power and photovoltaic(PV)plants is presented in this paper.It has been applied for optimizing the control parameters of the load frequency controller(LFC)of the multi-source power system(MSPS).The MSPS includes thermal,gas,and hydro power plants for energy generation.Moreover,the MSPS is integrated with renewable energy sources(RES).The MVO algorithm is applied to acquire the ideal parameters of the controller for controlling a single area and a multi-area MSPS integrated with RES.HVDC link is utilized in shunt with AC multi-areas interconnection tie line.The proposed scheme has achieved robust performance against the disturbance in loading conditions,variation of system parameters,and size of step load perturbation(SLP).Meanwhile,the simulation outcomes showed a good dynamic performance of the proposed controller.

Study on Energy Conservation of External Wall Structure of Rural Houses in Cold Area

According to the status of the northern rural architecture analysis, the prevalence of high energy consumption, low comfort features, it is essential to design energy-efficient buildings in rural areas. Article research and analyze the layout of rural buildings, a variety of energy-saving technologies and building design measures development and utilization of new energy sources and other issues that can make rural buildings to save energy and improve the environmental quality of living of farmers. It also put forward some reasonable suggestions for carrying out residential building energy efficiency in cold areas.

水电富集区域电量送出能力提升的源储协同优化运行方法

针对可再生能源出力的强波动性、间歇性导致电力外送断面出现短时间尺度重过载、清洁能源送出消纳困难等问题,提出了水电富集区域电量送出能力提升的源储协同优化运行方法,该方法定义了衡量断面外送电量的量化指标,以系统发电成本最小、断面输出电量最大、储能消耗电量最小为目标,建立多目标优化模型,通过求解帕累托前沿,结合熵权双基点法,得到折中最优点。对我国某省级电网算例的仿真结果表明,该方法可有效通过水电、火电和储能的协同优化运行提升断面外送可再生能源的发电量,在保证系统安全稳定运行的前提下提升了可再生能源的消纳率。

基于多因素正交实验的关中民居节能优化分析

为解决陕西关中地区民居建筑围护结构热工性能不佳、采暖效率低的问题,本文以关中典型民居为研究对象,从建筑外围护结构和窗墙比方面展开关中民居节能优化分析。结合现场测试调研数据,通过DesigBuilder软件模拟能耗,探究围护结构构造和窗墙比大小对关中民居建筑热负荷、冷负荷、全生命周期投资收益比的影响,采用多因素正交实验方法耦合分析各优化方案的节能性与经济性,选出最适宜的综合节能策略。结果表明关中民居最佳节能优化策略为外墙加设80 mm厚EPS保温层、屋顶加设60 mm厚XPS保温层、外窗改用普通中空5+9A+5玻璃、北向窗墙比0.1、南向窗墙比0.3。优化后关中典型民居节能61.98%,冬季典型气象日室内平均温度上升2.78℃。

面向乡村建筑分时分区用能需求的零碳综合能源系统设计优化

基于传统最大负荷原则设计的综合能源系统容量通常远大于建筑的实际用能需求,导致以户为单位的乡村综合能源系统设计缺乏针对性和适用性。该文通过现场调查和数值模拟的方法精细化乡村建筑的实际用能需求,基于农户用能行为制定4种用能策略。并构建考虑农户经济水平和零碳约束的综合能源系统设计优化模型,采用两阶段带精英策略的快速非支配排序遗传算法(Top-NSGA-II)进行多目标优化问题的求解,获得不同策略下的最佳设计方案。结果表明,与基于建筑全空间连续动态负荷的设计优化方案相比,基于分时分区用能需求的设计方案年总成本降低13.58%,系统购电比例降低7.89个百分点,实现全年净零碳排放。

考虑供需灵活性量化的源-荷-储多时间尺度协同优化

风电、光伏类新能源出力具有随机性和波动性,且随着新能源渗透率的增高,系统灵活性需求也激增。在此背景下,考虑源-荷-储灵活性资源互动,对新能源高渗透系统源-荷-储协同运行问题展开研究。首先,引入向上、向下裕度量化系统灵活性,并利用灵活性供需平衡机理动态评估灵活性需求;其次,提出一种考虑供需灵活性的多时间尺度协同运行方法,构建典型周、典型日、典型时段运行模拟模型,并将碳交易收益和灵活性不足惩罚成本纳入优化目标,考虑调峰需求和爬坡容量对典型时段灵活性的影响,采用多级优化、逐级细化的思路求解模型。算例分析表明,所提方法在保障系统环保性和经济性的基础上,提升了系统各个时间尺度上的灵活性容量,可以促进系统新能源消纳。

计及设备变负载率特性的综合能源系统“源-荷-储”协同优化调度策略

针对传统综合能源系统忽略设备变负载率特性建模而导致调度方案经济性降低等问题,提出了一种计及设备变负载率特性的综合能源系统“源-荷-储”协同优化调度策略。文章基于对系统“源-储”两侧设备运行特性的研究,建立了设备的变负载率模型,以减少传统恒定效率模型带来的调度误差;引入主从博弈理论分析了综合能源系统供需两侧之间的交互过程,建立了计及设备变负载率特性的供需两侧主从博弈模型,进而证明了所提模型存在唯一博弈均衡,并提出相应求解流程;最后,对某园区综合能源系统进行仿真分析。结果表明:所提策略能够实现综合能源系统“源-荷-储”协同运行,提升供给侧与需求侧的经济效益。