Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

Two-stage stochastic approach for spinning reserve allocation in dynamic economic dispatch

A novel approach was proposed to allocate spinning reserve for dynamic economic dispatch.The proposed approach set up a two-stage stochastic programming model to allocate reserve.The model was solved using a decomposed algorithm based on Benders' decomposition.The model and the algorithm were applied to a simple 3-node system and an actual 445-node system for verification,respectively.Test results show that the model can save 84.5 US $ cost for the testing three-node system,and the algorithm can solve the model for 445-node system within 5 min.The test results also illustrate that the proposed approach is efficient and suitable for large system calculation.

Dynamic Economic Dispatch for Wind Power System Considering System Security and Spinning Reserve

In this paper, dynamic economic dispatch model is proposed for power systems with bulk wind power integration. The wind turbine generators are assumed to partially undertake the spinning reserve for the thermal generator. A double-layer optimization model is proposed. The outer layer use the differential evolution to search for the power output of thermal generators, and the inner layer use the primal-dual interior point method to solve the OPF of the established output state. Finally, the impact of spinning reserve with wind power on power system operating is validated.

Dynamic Economic Dispatch for Microgrids Including Battery Energy Storage

Microgrids integrate distributed renewable energy resources, controllable loads and energy storage in a more economic and reliable fashion. Battery energy storage units are essential for microgrid operation, which make microgird become a strong coupling system in the time domain. Hence, the traditional methods of static dispatch are no longer suitable for microgrids. This paper proposes a dynamic economic dispatch method for microgrids. Considering microgrid as a discrete time system, the dynamic economic dispatch is to find the optimal control strategy for the system in finite time period. Based on this idea, the dynamic economic dispatch model for microgrids is established, and then the corresponding dynamic programming algorithm is designed. Finally, an example of microgrid is given, and the dynamic economic dispatch results are compared with that of the static dispatch. The comparison confirms the effectiveness of the proposed dynamic dispatch method.

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

Reference Point Based TR-PSO for Multi-Objective Environmental/Economic Dispatch

A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.

Security and Stability Aspects of Multi Objective Dynamic Economic Dispatch with Renewable Energy and HVDC Transmission Lines

Renewable sources of energy are being integrated into the power grids due to their economic and environmental merits as compared with the traditional fossil-fuel-fired power generation. However, their significant penetration demands a thorough research in terms of system reliability, that is, security and stability. In this paper, Security Constrained Multi Objective Dynamic Economic Dispatch (SCMODED) problem considering cubic thermal cubic cost function, wind, solar penetration, cubic transmission power losses and cubic emissions cost function as objectives is first formulated. Both HVDC and HVAC lines are included in their formulation. Various approaches like probabilistic load flow (PLF), scenario based method, participation factors and Harmony Search algorithm etc. are employed in the solution process. Security and stability effects of renewable energy (RE) penetration are investigated and analyzed. The simulated results reveal that RE penetration leads to reduced cost and emissions and increased security concerns. Further, there is increased power system instability and hence increased load shedding so as to help the power system attain steady state stability. Inclusion of HVDC lines facilitates rapid and fast control to increase the transient stability limit by the action of the converter ignition angle (CIA) and converter extinction angle (CEA).

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.

Spatial spillover effects of official development assistance on environmental pressure in sub-Saharan African(SSA)countries

Impoverished sub-Saharan Africa(SSA)is under increasing environmental pressure from global environmental changes.It is now generally accepted in academic circles that economic development in SSA countries can cause environmental pressure in other countries.However,there is research gap on the impact of economic assistance on environmental pressure in SSA countries and whether economic assistance causes spatial spillovers of environ-mental pressure between SSA countries.To better understand the impact of economic assistance on environmental pressures in SSA,a dynamic spatial Dubin panel model was developed.It helped us explore the spatial spillover effects of economic assistance on environmental pressures in recipient countries based on the panel data from 34 SSA countries.The results show that economic assistance had a positive stimulating effect on environmen-tal pressures of recipient countries,which means that the degree of human disturbance to the environment has deepened.Due to the regional correlation effect,neighboring countries were saddled with environmental pres-sures from the target country.Moreover,environmental pressures have time inertia,which can easily produce a snowball effect.The decomposition of effects shows that the impact of economic assistance on environmental pressures is relatively minor.Environmental pressures have spillover effects,so to deal with diffuse risks,joint regional prevention and control policies should be developed.

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements.These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate limits.The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times.In this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model.Self-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch problems.Numerical results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method.

PREMISE Insect Model： Integrated Population Dynamics Model for the Ex-ante Evaluation of IPM against Insect Pest

Codling moth Cydiapomonella L. is the most serious pest of apple and pear worldwide and causes damage and decreased yields. To minimize this risk, IPM tools can be applied to reduce the use of chemicals. A cost-effective application of IPM depends on the number of insects at the time of application. Several conditions and factors influence the lifecycle and numbers of generations within a year. In order to perform ex-ante evaluations of the cost-effectiveness of IPM measures against pest insects, the generic PREMISE insect model has been developed, which integrates insect＇s population dynamics, the climate and other conditions, applied measures, economics, environment and human health （residues）. This paper describes these components of the PREMISE insect model, and how these components interact to assess the cost-effectiveness of IPM measures and the effects on the environment and human health. Codling moth is used as a case study for this generic model.

考虑超碳需求响应的综合能源系统低碳优化调度

西北地区新能源发展迅速,充分利用当地独有的光热资源优势,同时结合火电低碳化改造与新能源协同运行,有利于推动当地能源系统绿色低碳转型。为尽最大限度地提升系统的减碳能力,提出一种基于源荷协同降碳的超碳需求响应模型,将动态碳排放因子作为分时电价的惩罚因子,从而将源侧碳信号传递至荷侧,驱使用户侧进行低碳性状态转移。首先,在日前调度阶段,构建预调度-再调度两阶段调度运行机制,再调度根据预调度的系统状态信息进行超碳需求响应,来深度降低系统碳排量。其次,将光热电站引入综合能源系统,与风电场、碳捕集电厂协同运行,从而构建高比例新能源场景,来验证超碳需求响应在此场景下的减碳效益。最后,建立了基于超碳需求响应的预调度-再调度两阶段低碳调度模型。经算例仿真分析表明,所提源荷协同降碳的新思路能有效提高系统的减碳能力,深入挖掘系统的降碳空间,提升系统的经济效益。

基于动态掺氢策略的综合能源系统低碳经济调度

针对现有综合能源系统中传统碳捕集电厂灵活性较差,电负荷系数、管道长度和管道运维成本等因素对计及气网掺氢的综合能源系统调度的影响尚未明确等问题,提出了一种基于动态掺氢策略的综合能源系统低碳经济调度模型。首先,建立综合灵活碳捕集电厂模型,对二氧化碳的吸收与再生环节进行解耦;其次,建立基于动态掺氢的管道运维成本模型,实现掺氢比的动态调整,提高系统调度的灵活性;然后,引入阶梯式碳交易机制,构建考虑气网掺氢管道运维成本的综合能源系统低碳经济调度模型,利用McCormick松弛方法将非线性问题转化为线性问题,并进行求解。最后,算例仿真结果表明:气网各节点处管道运维成本系数与气体体积、输送距离等因素相关,应综合考虑上述因素对气体定价;在综合能源系统调度中综合考虑电负荷系数、气网管道长度等因素合理选择气网掺氢比上限、制定动态掺氢策略,系统的经济效益与环境效益分别提高了44.1%、47.8%。

中国出口经济利益与环境成本绩效评价

文章利用多区域投入产出模型并借助“贸易条件”概念,编制相对出口增加值率和出口隐含碳污染条件指数;构建出口经济利益-环境成本增长弹性系数,对中国通过出口获得的经济利益与付出的环境成本进行绩效评价。结果表明:加入世界贸易组织初期,中国总体出口经济利益处于有利地位,但此后呈现持续下滑态势;中国总体出口环境成本相对较大,但呈现相对改善态势;中国对贸易伙伴出口的经济利益与环境成本绩效总体呈现优化态势。

基于改进堆优化算法求解电动汽车并网动态经济调度

针对堆优化算法HBO处理含电动汽车的动态经济调度问题时存在收敛慢和精度低等问题,提出一种改进的堆优化算法RDHBO。首先,在RDHBO中引入了最优成员区域搜索和双种群交互策略。前者引导最优成员移动到更有希望的区域,提升了算法的收敛精度和收敛速度;后者充分利用被淘汰的劣势个体,丰富了种群的多样性,避免了算法陷入局部最优。然后,将RDHBO应用于四种充电场景的10机组电动汽车动态经济调度问题。仿真结果表明,与已有的代表性方法相比,RDHBO在产生低燃料成本和稳定性方面具有很强的竞争力。最后,对RDHBO的两种改进策略进行了消融实验,验证了两种改进策略的有效性。

考虑碳-绿证耦合机制的电-气-热综合能源系统分布式鲁棒低碳经济调度

为了保护综合能源系统中不同运营主体的信息隐私,权衡系统运行低碳性和经济性,解决天然气网络鲁棒模型难以求解的问题,提出了考虑碳-绿证耦合机制的电-气-热综合能源系统分布式鲁棒低碳经济调度方法。考虑天然气网络和热力网络的动态特性、碳-绿证耦合机制,构建综合能源系统动态低碳经济调度模型;为了保护各网络运营商的数据隐私,根据能量耦合关系对综合能源系统解耦,建立电-气-热网络的分布式协同优化模型;在此基础上,考虑风电出力和多能负荷的不确定性,提出基于一致性交替方向乘子法的分布式鲁棒优化框架,并利用二阶锥对偶理论与交替优化方法实现含二阶锥约束和二元变量的鲁棒子问题求解。以修改的IEEE 39节点电力网络、比利时20节点天然气网络和15节点热力网络为算例进行仿真分析,验证所提方法能够在源荷不确定性条件下实现综合能源系统各网络分散自治运行,同时兼顾系统运行的低碳性和经济性。

Reserve Constrained Dynamic Economic Dispatch with Valve-point Effect:A Two-stage Mixed Integer Linear Programming Approach

This paper proposes a deterministic two-stage mixed integer linear programming(TSMILP)approach to solve the reserve constrained dynamic economic dispatch(DED)problem considering valve-point effect(VPE).In stage one,the nonsmooth cost function and the transmission loss are piecewise linearized and consequently the DED problem is formulated as a mixed integer linear programming(MILP)problem,which can be solved by commercial solvers.In stage two,based on the solution obtained in stage one,a range compression technique is proposed to make a further exploitation in the subspace of the whole solution domain.Due to the linear approximation of the transmission loss,the solution obtained in stage two dose not strictly satisfies the power balance constraint.Hence,a forward procedure is employed to eliminate the error.The simulation results on four test systems show that TSMILP makes satisfactory performances,in comparison with the existing methods.

基于碳势-能源价格双响应的综合能源系统低碳经济调度

利用电-气-热多元负荷节点碳势引导需求响应是实现综合能源系统(IES)低碳运行的有效手段。IES中多能流耦合关系复杂、动态特性交织,碳排放特性难以准确表达。文中针对IES中低碳运行潜力挖掘不足的问题进行了研究,提出了一种基于节点碳势-能源价格双响应的经济调度方法,用于IES源-荷协同,实现低碳运行。一方面,通过剖析气、热动态特性对碳排放流的影响机理,引入动态特性等价替换思想,得出计及动态特性的碳排放流模型,进而得到负荷节点的碳势,实现碳排放责任的分摊;另一方面,从碳视角出发,考虑多元负荷的碳排放责任,设计了基于多元负荷节点碳势-能源价格双响应的IES源-荷低碳互动机制,充分挖掘IES中多元负荷的低碳潜力。通过对IES中碳排放产生、传输、消费全过程的追踪与计量,制定兼顾经济性与低碳性的调控策略。以电网14节点-热网6节点-天然气网6节点(E14-H6-G6)和E57-H12-G12测试系统为例,验证了所提模型和方法的有效性。

考虑源荷双重不确定性的多源系统鲁棒模糊动态综合环境经济调度

“双碳”目标下,以风电、光伏、光热发电为主的源端新能源规模化并网以及荷端供给需求和种类的多样性使得系统源荷平衡受到极大影响,给新型电力系统运行带来严峻挑战。基于此,以风电-光伏-光热-火电联合发电系统为研究对象,提出一种考虑源荷双重不确定性的鲁棒模糊动态综合环境经济调度模型。首先,分别基于鲁棒理论和模糊理论对多源系统源荷不确定因素建模,同时构建鲁棒模糊数学模型并推导其等价确定模型;其次,综合考虑各种环境经济因素,构建多源系统鲁棒模糊动态综合环境经济调度模型;然后,根据其等价确定转化思想对模型进行转化并采用基于差分进化的粒子群算法求解;最后,基于新疆哈密电网实际数据和改进IEEE-39节点系统,验证所建模型的寻优效果在环境经济上的可行性、有效性及优越性。

Q学习差分进化算法求解热电动态经济排放调度

热电联产动态经济排放调度同时考虑了燃料成本花费和污染气体排放两个目标值,且下一时间段的热电产量受当前时间段热电产量的影响,这是近年来电力系统运行中的一个重要问题。文中提出一种基于Q学习强化多目标差分进化(Q Learning Multi-Objective Differential Evolution,QLMODE)算法,以此求解热电联产动态经济排放调度(Combined Heat and Power Dynamic Economic Emission Dispatch,CHPDEED)问题。在QLMODE中,采用Q学习技术调整算法的比例因子参数,即在迭代过程中利用子代解和父代解之间的支配关系确定动作奖励和惩罚,并通过Q学习调整参数值,以获得最适合环境模型的算法参数。文中将所提QLMODE用于求解11机组和33机组的热电联产动态经济排放调度问题。仿真结果表明,与4种成熟的多目标优化算法相比,QLMODE算法燃料成本最小,污染气体排放最少,收敛性和多样性指标优于其他4种算法,且QLMODE在两组问题上都获得了更好的Pareto最优前沿。